Token-Efficient Item Representation via Images for LLM Recommender Systems
Authors: Kibum Kim
Sein Kim
HongSeok Kang
Jiwan Kim
Heewoong Noh
Yeonjun In
Kanghoon Yoon
Jinoh Oh
Julian McAuley
Chanyoung Park
Venue: ICLR 2026 Poster (2026)
Keywords: Recommender Systems
Large Language Models
Sequence Modeling
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: By using images of items interacted with by users for LLMs, we aim to enhance the efficiency and effectiveness of LLM-based recommender systems.

Abstract: Large Language Models (LLMs) have recently emerged as a powerful backbone for recommender systems. Existing LLM-based recommender systems take two different approaches for representing items in natural language, i.e., Attribute-based Representation and Description-based Representation. In this work, we aim to address the trade-off between efficiency and effectiveness that these two approaches encounter, when representing items consumed by users. Based on our observation that there is a significant information overlap between images and descriptions associated with items, we propose a novel method, **I**tem representation for **LLM**-based **Rec**ommender system (I-LLMRec). Our main idea is to leverage images as an alternative to lengthy textual descriptions for representing items, aiming at reducing token usage while preserving the rich semantic information of item descriptions.
Through extensive experiments on real-world Amazon datasets, we demonstrate that I-LLMRec outperforms existing methods that leverage textual descriptions for representing items in both efficiency and effectiveness by leveraging images. Moreover, a further appeal of I-LLMRec is its ability to reduce sensitivity to noise in descriptions, leading to more robust recommendations.

Published as a conference paper at ICLR 2026
TOKEN-EFFICIENT ITEM REPRESENTATION VIA IMAGES FOR LLM RECOMMENDER SYSTEMS
Kibum Kim _ Sein Kim 1.
HongSeok Kang'.
Jiwan Kim
Heewoong Noh 1.
Yeonjun In I.
Kanghoon Yoon 1.
Jinoh Oh
Julian McAuley 3.
Chanyoung Park
KAIST Amazon Alexa
UC San Diego
ABSTRACT
Large Language Models (LLMs) have recently emerged powerful backbone for recommender systems. Existing LLM-based recommender systems take two different approaches for representing items in natural language, i.e., Attribute- based Representation and Description-base Representation. In this work, we aim to address the trade-off between efficiency and effectiveness that these two ap- proaches encounter, when representing items consumed by users. Based on our observation that there is a significant information overlap between images and descriptions associated with items, we propose novel method, Image represen- tation for LLM-based Recommender system (I-LLMRec). Our main idea is to leverage images as an alternative to lengthy textual descriptions for representing items, aiming at reducing token usage while preserving the rich semantic informa- tion of item descriptions. Through extensive experiments on real-world Amazon datasets, we demonstrate that I-LLMRec outperforms existing methods that lever- age textual descriptions for representing items in both efficiency and effectiveness by leveraging images. Morcover, a further appeal of I-LLMRec is its ability to re- duce sensitivity to noise in descriptions, leading to more robust recommendations. Our code is available at https//giitub.com/rlgg
http://gihbboooo///////////oo/h/ 107/tocch--LLLMMR...
INTRODUCTION
LLMs, which have recently shown remarkable performance in various NLP tasks by leveraging strong semantic reasoning and world knowledge, have inspired research into their application across diverse domains, including recommender systems (Kim et al., 2024a; Chen et al., 2024; Kim et al., 2024b; Xie et al., 2024). Especially, recent studies explore the replacement of traditional collabo- rative filtering models (e.g., SASRec (Kang & McAuley, 2018)) with LLMs as the backbone for recommendations (Lin et al., 2024b; Bao et al., 2023; Tan et al., 2024). To this end, they typi- cally transform each item in a user's s interaction history from a traditional numerical ID into natural language (e.g.. titles) and arrange them into sequences within the input prompt.
The key to LLM-based recommender systems lies in effectively representing items in natural lan- guage to capture user preferences based on LLM's comprehensive understanding of user interaction history. In this regard, existing studies that represent items in natural language can be categorized into the following two main approaches: 1) Attribute-Based Representation approach is to com- bine simple attributes such brand and category with the item title to represent the item (Bao al., 2023; Li et al., 2023a; Tan et al., 2024). For example, TALLRec (Bao et al., 2023) and TransRec (Lin et al., 2024b) enhance the understanding of user preferences by adding attributes, thereby facilitat- ing a multifaceted understanding of items 2) Description Bassd Representation approach is to use detailed item descriptions' to preserve rich textual item semantics that the LLM could capture user preference in more fin-grainee manner. For example, TRSR (Zheng et al., 2024b) summa- rizes full descriptions and provides them to the LLM for recommendation, thereby overcoming the limited item semantics provided by attributes only and addressing the input length constraints of
"Corresponding Author
'While attributes the high-level, general features in few keywords (e.g.. Apple), descriptions generally provide item-specific details (c.g., is slim metallic hody, 13-inch Liquid Retina, and black keyboard...).

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Published as a conference paper at ICLR 2026
Frequency
2500
2000-
3000
1500-
Full description
Aertuum
Summen red tescription
HitB5
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800
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0.0
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0.2
0.3
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0.5
Input token length for user interaction history
Inference time [seconds]
Similarity Score
(a)
(b)
(c)
Figure I: (a) Histogram of the input token length required to represent a user's item interaction history across different item expression approaches. (b) Recommendation performance (Hit @) and Inference Time (seconds/100 users) for different item representation approaches. For (a) and (b), we use the Amazon Sports dataset for analysis. (c) Cosine similarity between item maggeddescription pairs in Amazon Sport and Art datasets and image-caption pairs in the COCO dataset using CLIP.
LLMs. Overall, these approaches demonstrate the potential of natural language in enriching item representations for LLM-based recommendation.
However, argue that when representing items in natural language, there is inherent trade-off between efficiency and effectiveness. Specifically, the atribute-Basse Representation approach is efficient since it requires fewer tokens to add item attributes than to add the entire item descriptions, which shortens the input length for the LLM. However, it sacrifices the effectiveness of under- standing user preferences due to the limited item semantics that can be contained in relatively short attributes. On the other hand, the Description-Baasee to the longer input length required for detailed item descriptions, whereas the effectiveness of un- derstanding user preferences is improved by incorporating richer textual semantics of items. In fact, as shown in Figure I(a), the input length required for LLMs to represent user's 's item interaction history significantly varies depending on how the item is represented. That is, the Description-B Based Representation approach (i.e., Summarized description and Full description) demands much longer input token lengths than the Attribute-Based Representation approach (i.e., Attribute), resulting in the increased computational complexity.
Representation approach is less efficient due
To better understand this trade-off, in Figure 1(b), we extend our analysis beyond a simple compari- son of input token length and examine the computational complexity of different item representation approaches (i.e., efficiency) along with their recommendation performance (i.e., effectiivnnss). Note that we use the same LLM backbone (i.e., Sheared I.LMAA-277B (Xia et al., 2023)) and adopt the same recommendation protocol for fair comparisons (See Appendix B regarding the details of the recommendation protocol). We observe that the Attribute- based Representation approach (blue cir- cle) achieves the inference time more than 2.5 times faster than the Description- -based Representation approach (green and black). However, this comes at the expense of a performance reduction of over 13% due to the lack of rich item semantics contained in attributes compared with descriptions. Fur- thermore, summarizing full descriptions reduces inference time but compromises performance due to the information loss during the summarization. In conclusion, the trade-off can be summarized as: richer item representation provided to the LLM improves effectiveness but decreases efficiency. However, this trade-off is unavoidable by nature if items are represented with natural language.
In this paper, focus addressing this trade-off based on our observation that there is signifi- cant information overlap between images and descriptions associated with items. Specifically, when measure the similarity between item image- descrrttiiiitinn pairs in a real-world e-commerce dataset (i.e., Amazon Sport and Art (Ni et al., 2019)) using a vision-languag model (i.c., CLIP (Radford et al. 2021)) in which image and language spaces are jointly embedded, we found that the similar- ity is surprisingly high. Specifically, Figure I(c) shows that the average similarity for the Amazon Sport and Art datasets is around 0.31. To better understand this similarity level, we conducted the same experiment with the COCO caption dataset (Lin et al., 2014), a well-curated image-caption pair dataset widely used in vision linion-language research. Specifically, we measured similarities for both positive (i.e., image-caption) and negative (i.e., maggerrandolly sampled unrelated caption) pairs. Given that the negative pairs have an average similarity of only 0.07, the positive pairs' average sim- ilarity of around 0.26 can be considered an indicator of high similarity. Furthermore, the similarity
TTo summarize the full description. we adopt an approach similar TRSR (Zheng al., 2024b) and similar prompt. For more details, please refer to Appendix A.
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value of 0.31 between item image-desene desee-desciittion pairs, which is even higher, highlights significant information overlap between images and their associated descriptions".
Building on this observation, we propose Image representation for LLM-based Recommender sys- tem (l-LLMRec), which addresses both efficiency and effectiveness of existing L.LM-based recom- mender systems by leveraging images as an alternative to lengthy textual descriptions for the item representation, aiming at reducing token usage while preserving the rich semantic information of item descriptions.. The main technical challenge lies in the misalignment between the item image space and the language space. To this end, we adopt a learnable adaptor for visual features fol- lowed by a technique to bridge the gap between the two spaces (i.e., Recommendation-oriinntee Image-LLM Semantic Alignment (RISA) module), which facilitates the training of the adaptor with carefully crafted prompts tailored to the recommendation context. This ensures the LLM to cap- ture rich item semanties through images with only few tokens, thereby effectively and efficiently capturing user preferences.
Through extensive experiments real-world Amazon datasets, we demonstrate that proposed method using images instead of item descriptions is superior in both effectiveness and efficiency. Specifically, I-LLMRec improves inference speed by approximately 2.93 times compared to the Description-base Representation approach, while achieving a 22% performance improvement over the Attribute-ban based Representation approach (Figure (bb)). As a further appeal, I-LLMRec facilitates robust recommendations by mitigating sensitivity to noise associated with item descriptions as leverages images. Our main contributions can be summarized as follows:
We identify a trade-off between efficiency and effectiveness when expressing items in natural language.
Based on the observation that there is significant overlap between item image-description pairs, we propose a novel method, called I-LLMRec, which utilizes images instead of textual descrip- tions to efficiently and effectively capture user preferences.
Our extensive experiments demonstrate that I-LLMRec outperforms the various natural language- based representation approaches in both effectiveness and efficiency.
2 PRELIMINARIES
Here, we introduce the task formulation and examine the complexity of different item expression approaches.
Task Formulation. Throughout this paper, mainly focus on sequential recommendation, it closely aligns with real-world scenarios (Tian et al., 2022; Xie et al., 2022). Let 24 and I denote U has the historical item interaction sequence
the set of users and items, respectively. A user denoted as S,=[ú1; i2; ..., i..... is.. where ig denotes the k-th interacted item and [S..] is the number of items in the interaction sequence. The goal of this task is, for each user u, to predict the next item i|s.,|11 to be consumed by the user based on the user interaction history S..
Representing Item Semantics. With the advancement of LLMs for recommender systems. there is a growing need help them understand the semantics of items to capture user preferences. this regard, we categorize the multiple types of information to recommend an item i's semantics into [I, D., A, where I,, D., and A; are an item image, textual description', and attribute, respectively.
Comparison of Complexity. Let f(.) denote the transformation function for the LLM to convert the representation of item semantics into input tokens, and let [](()] denote the number of input tokens. Specifically, for D; and A; that are in natural language, ff refers to tokenizing the natural language and converting them into the word embeddings. Mcanwhile, for an image 1, f involves extracting the visual features using a pretrained vision encoder (c.g., CLIP- ViT (Radford et al., 202 1)), followed by an adaptor resize their feature dimensions for compatibility with the LLM (Liu et al., 2024a; Li et al., 2023b). The complexity of LLM operations for each representation
'Such an observation was consistently observed across other real-world datasets as well as another CLIP variant model (See Appendix C and F.4).
*Some text-specific information that is missing an image may he lost, hut we find that in Section 4.2, this information has surprisingly little impact on recommendations.
'To avoid confusion, will henceforth refer to the description summarized description instead of full description unless stated otherwise.
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is ((IF)))))) O(( and (((ss(A)))Ss.)))) where If(D.) >> I-(A,)) This is derived based on the complexity of Transformer, i.e., O(nåd), where n the input token length and d is the feature dimension of the LLM. It reveals that the complexity of the description- based approach is not merely being incurred with a single item; rather, increases with the user sequence length [ and its complexity even grows quadratically, making this approach impracti- cal. Therefore, in this work, we propose to lower the complexity of expressing an item by leveraging
If(I,) 1 16 while preserving the rich semantics of the item description. This is based on our observation that there is a significant information overlap between the item image-description pairs (Figure I(c)).
its associated image using only single token (i.e., If(D.) > If(A,)
3 PROPOSED METHOD: I I-LLMREC
In this section, we provide a detailed explanation of I-LLMRec. After introducing the initial recommendation--elatede task prompt, we begin by encoding the images of items consumed by users, and mapping them to an LLM through a learnable adaptor (Section 3.1). However, since these mapped visual features are not inherently aligned with the language space, we propose the Recommendation-orinne
Image-LL.M Semantic Alignment (RISA) module that trains the adap- tor with carefully crafted prompts tailored for the recommendation context, resulting in effectively bridging the alignment gap (Section 3.2). Meanwhile, we propose the REtrieval- based Recommen- dation via Image features (RERI) module, a training approach that enables the LLM to directly perform recommendations from the item corpus based on the visual features (Section 3.3). Finally, we describe the overall training objectives and inference for recommendation (Section 3.4). Figure 2 shows the overall framework of I-LLMRec.
3.1 MAPPING ITEM IMAGES TO LLM
In this section, our goal is to map items consumed by users to an LLM using only few tokens, allowing us to capture user preference efficiently and effectively. Specifically, given the interaction history of user u, i.c., [i,, i2. .... I-.. and a target item i|s_). we convert the interaction history into a sequence of item images [I,, ...,
11) x3, where H and W are the image height and width, respectively. Then, we adopt a pretrained visual encoder V of a vision- language model to extract the visual features of each item, defined as = V(I,) E Rd,, This IR ([S_|_ where d, is the process makes the sequence of visual features [U;, 1/a: Pis..i dimension of the visual feature.
.... I .1
Adaptor network for visual features. However, since the visual feature dimension d, is different from that of the LLM feature d, and their spaces are inherently misaligned, we introduce an adaptor network M. Rdd. + Rd make visual features compatible with the LLM's feature dimension, allowing vy to be interactive with word embeddings within the LLM's input layer. More precisely, we process each item's visual feature v; through the adaptor M, transforming it into sequence of visual features that match the dimensionality of the LLM. Formally, the sequence of mapped visual features is defined as S = [M Uba3.., Dij..il. where v; M(:)) Rdd
Designing prompt to represent user's item interaction history. To represent user's item interaction history with visual features, we carefully design a prompt, en- abling the L.MM comprehensively understand the item semantics. Specifically, for each item i in a user's item interaction history, we represent it in the prompt as P; (Title: ITEM TITLE, Visual Representation [VISUAL]]. where P; is the representation of item TITLE is the title of the item i, and [VISUAL] is the placeholder of item i's visual feature U. More precisely, before being forwarded to the LLM's transformer layer, [VISUAL] is replaced with ün while the natural language in P; is converted into word embeddings. Note that following prior studies (Bao et al., 2023; Li et al., 2023a), we use the item title instead of the item's 's numerical ID. By enumerating P. over the item interaction history of user u (i.e.. PP.,.P....... represent the user interaction based on item images where each image expressed using a single token, allowing the LLM to efficiently and effectively capture the user preference.
i, -TTEL
P.y.... P.js, -,]), we
3.2 RECOMMENDATION-OREEEE
IMAGE-LLM SEMANTIC ALIGNMENT (RISA) MODULE
To make the LLM effectively capture user preferences from item images, it is crucial to align visual features with the language space in a meaningful way, requiring adequate supervision of the adaptor
IIn average, If() and (A.)) are approximately 160 and 10 tokens, respectively.
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brand nest fer consered a wour.
Large Language Model
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Adaptor (Af)
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[a] Model Training
(b) Inference
Figure 2: Overall framework of I-LLMRec. User-interacted item images are mapped into the LLM through an adaptor, which bridges the image and language spaces. To ensure alignment between two spaces, the adaptor is optimized via the RISA module. Furthermore, the recommendation process is formulated as a retrieval task via the RERI module.
network M. To achieve this, we optimize the adaptor network M by predicting the next item prop- erties based on the user interaction within structured prompt, thereby guiding the LLMs to capture user preferences in the context of recommendation through the visual features.
Specifically, craft prompt in the user interaction prompt, followed by a question regarding the next item (e.g., What brand is this user likely consume for the next item?). The target outpu corresponds to the answer that question (e.g., The brand likely to be consumed is 'WOLT"). To guide the LLM in considering various properties of the next item, incorporate multiple properties of the next item, including brand, category, title, and description. For each property, we design five different question templates (refer to Appendix D.I for complete templates). During each training step, we randomly select one of 20 possible question templates (4 properties x templates) and train to generate the corresponding output. The training objective is formulated as LRISA max Σl log(Po, M (yk |x, Bcx)), where M ===
"ttrget output" format. Here, the input consists of
0 is the parameter of the LLM that is frozen, is the target output, yı is the k-th token of and is the imput. Note that x incorporates the image features of items consumed by users, which are supervised under CRISA. Please refer to Appendix D.2 for further discussion of LLM fine-tuning.
3.3 RETRIEVAL--BSEE RECOMMENDATION VIA IMAGE FEATURES (RERI) MODULE
We found that existing studies LLM-based recommender systems often face challenges in pro- viding reliable and efficient recommendations since they typically recommend items by predicting the next item title (Kim et al., 2024b; Lin et al., 2024b; Tan et al., 2024) item tokens allocated as vocabuaayy (Wei et al., 2024; Li et al., 2023a; Zhu et al., 2024). Regarding reliable recom- mendation, the title prediction approach cannot guarantee that the recommended items exist within the item corpus, being likely to recommend non-existent items. Regarding efficient recommenda- tion, title prediction relies on computationally intensive beam search to recommend multiple items, while the item token prediction approach requires an oversized I.IM,, as expanding the item pool necessitates extending the LLM vocabulary set, which hinders scalability. To overcome these chal- lenges, we propose the REtrieval-baat formulates the recommendation task as retrieval task by leveraging the images to directly retrieve relevant items from the item pool. In the following, we describe how to obtain an LLM- --guddedd user representation for retrieval, and define a training objective that retrieves relevant items.
Recommendation via Image features (RERI) module. which
LLM-guided user representation. To obtain the LLM-guided user representation containing user preference, for each user u, we append an instruction prompt, i.c., Generate a recommendation token for the next item to be consumed, after the user interaction prompt [P.., .......,,PP -- This instruction prompt helps generate a recommendation token, guiding the model towards item retrieval-basee recommendations instead of item title generation based recommendation. At the end of this prompt, we append a learnable token [REC] that aggregates a user's item interaction history and instruction-related information, leveraging the LLM's contextual reasoning capabilities. Specif- ically, we utilize the last hidden state associated with the [REC] token (i.e., h(REC)) to obtain the aggregated information of user preference for retrieval.
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Training objective for retrieving relevant items. Given that h(RECC represents the user's pref- erence contained the interaction history of user u, i.e., [1,, f2, ..., i|S., I- I. we formulate the task of retrieving the target item iis.,, based on a scoring function T. unlike previous studies relying on generative task (i.e., item title generation). To this end, we use the visual feature of items as the item representations and compute the affinity score between those and (((EC)). However, as h( ((EEC) and the visual features lie in the different spaces, we employ projectors for each, ensuring that they are mapped into shared recommendation space. We denote the projectors and their out- puts as On
(ve s. ) where Film R + R F.! R. - R img F.. (h (((EE)))) o; lmg F
are the projectors, and d, is the feature dimension of shared recommendation space. Img and Img denote the projected representations of user u and item i terms of visual features, respectively. To optimize the retrieval task, we use the binary tross-entropp loss as follows:
Clmg RERI
Σ (r)(()(()))) + log(1 orr...)))
wEll
where Img = T(o lmg (9) ) oi. Img E R is the affinity score between Img and o with @ O;
denoting the dot product, and i is randomly selected negative item. It is important to note that since we formulate the training objective as a retrieval task, we can guarantee that the recommended item exists in the item corpus and do not need to extend the item tokens within the LLM, resulting in reliable and efficient recommendation process.
Extension multiple feature types. Our retrieval-based recommendation approach can seam- lessly incorporate multiple item feature types (c.g., ID-based embeddings and textual features) alongside visual features. Specifically, for a given item feature type denoted as s, we can simply in- tegrate it by introducing two additional projectors, F and F such that of = F (A((REC)) = Fj (IF"), ), where IF* represents the item feature corresponding to feature type (e.g.. V/is... II if * is the image type lmg), and of and of are the projected representations of user u and item in terms of feature type, respectively. Using o,, and of. we can then compute the binary cross-entropy loss CIERI following Equation 1.
Among various item feature types, we extract the ID-based item embeddings from a pretrained col- laborative filtering (CF) model (i.e., SASRec (Kang & McAuley, 2018)), thanks to its effectiveness in general recommendation tasks, especially for warm items (Kim et al., 2024b)?. Specifically, incorporate ID>baaedd item embeddings (i.e., C.,s.1 IF in addition to the visual features (i.e.,
Disull IF Im)), Furthermore, as item descriptions are readily available in real-world scenarios (Zheng et al., 2024h), we can easily incorporate textual features / extracted from full descriptions to further improve overall performance (i.e., ti,s.1 = =IF Text, The analysis of extension to various features is provided in Section 4.4.
3.4 TRAINING AND INFERENCE
Training. With the image, CF, and text feature types, we combine the training objective from RISA module and three training objectives from RERÍ module, training the adaptor M and projectors F and F (* = [lmg. CF, Textl) while freezing the LLM. The combined loss is denoted as:
Cfinat = CRISA + Clmg
CSF
LRERI
Inference. For retrieval-basee inference, we compute the affinity scores r Img r...- and Text using the scoring function T, in terms of visual, CF, and textual features, respectively. Specifically, the top- k relevant items for its./+1 is computed as rect Top-k(r Img + CF + Text), Vi E I, where rech is the set of recommended l: items for user u, and Top-k is function that extracts items with the highest top-k scores. Note that while the affinity scores across different features can be aggregated through various approaches, such as weight summation or product, we opt for simpler summation.
EXPERIMENTS
In this section, we conduct experiments to explore the following research questions:
In Section 4.4, we show that the utilization of ID-based item embeddings leads to more recommendations of warm items.
We opt fix all weights to avoid the computational burden of tuning over a large hyperparamter space.

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Table 1: Performance Comparison. A: AAtributed-basee Representation, CF CF-based Representa- tion (i.e., the CF item embedding is projected into the LLM), D: Description-based Representation, I: Image-based Representation.
Thitssot
Matric
Collaburative Filuering
1LM
Image LLM
GRUU4Rcc
VBPR
BERT4Ro: SASRe:
TALLRRc (A)
A-L.LMRe (CIF
TRSR (D)
UM/MP (T)
FILLMRoc (D+D LLLMPoc (1)
NDCGe5
0:2106
0.2369
0.2389
0.3129
0.2938
0.3352
0.1755
0.3364
0.3637
0.3711
Sport
NDCGG110
0.2820
0.2413
0.2094
0.3097
0.25/0
0.2991
0.3514
0.3841
0.3323
0.3801
0.4070
0.3083
0.4022
0.965
0,3029
0,4000
0.4003
044544
0,4071
0,4570
Have 10
0.3778
0.4008
0.3866
0.4760
0.4997
0.5056
0.5515
0A857
0.5694
0. 1,5699
NDCGes
0.2673
0.2321
0.2005
0.3753
0.3477
0,3660
0.3802
0.3710
3008
0.3956
Grocesy
NDCG0010
0.3003
0.3609
0.3171
0.263
0.3834
0.3517
0.4096
0.4684
0.4589
0.3874
044955
0.4823
014184
0.4917
04506
0.011
0.5037
044288
0.4332
0,5069
FEire 10
0,4726
0,4252
0,4835
0.5746
0,5815
0.5864
0.4099
0,5439
0.6311
0.6232
NDCGe5
0.3118
0,3710
0.3626
0.4561
0,4572
0.4622
0.1758
04465
0.4796
0,6539
Am
NDCG6110
0.3481
0.4044
0,4656
0,4455
0.5374
0.4260
0,8944
0,5663
0.4981
0.5681
0.100
0.5841
0.5315
0.5160
0,5902
0.5191
0.558.7
Hiide 10
0.5203
0.6301
0,6074
NDCGes
0.2023
0.2319
0.3299
0.3731
3404
1,5686
0.3900
Phone
NDCCG010
0.2577
0.2353
0.2064
0.4366
0.3658
0.8986
0,4148
0.5757
0.4309
0.5156
0,4320
FE
0.3953
0,3698
0,4255
0.5478
0.6305
0.5761
0.640
0.5569
0.6463
0.6448
RQ1: How does I-LLMRec perform compared to the CF and LLM-based recommender models?
RQ2: How well does I-LLMRec offer strengths (i.e., efficiency, effectiveness, and robustness) by utilizing images rather than lengthy descriptions?
RQ3: How does each module (i.e., RISA and RERI) contribute the I-LLMRec?
RQ4: How handle when item images are missing?
4.1 EXPERIMENTAL SETTING
Datasets. For evaluation, we use four categories from the Amazon dataset (Ni et al., 2019): Sports, Grocery, Art, and Phone. These datasets contain the item titles, attributes (e.g., brand and category), detailed textual descriptions, and images representing items. Following a prior study (Wei et al., 2024), we filter out users and items with fewer than interactions ensure data quality. We sum- marize the data statistics in the Appendix E.I.
Evaluation Protocol. Following the leave-one-out protocol (Kim al., 2023; Kang & McAuley, 2018), we use each user's most recent interaction as the test set, the second most recent interaction as the validation set, and the remaining interactions as the training set. For evaluation metrics, we utilize Hit Ratio (Hit@ k) and Normalized Discounted Cumulative Gain (NDCG@) k) with k: = 5, 10. Hit@k is measures whether the ground-truth item appears in the recommended list (i.e., rect) while NDCG@ evaluates its ranking position. To reduce computational complexity during evaluation for each user, we randomly select 100 negative items that the user has not interacted with, add the ground-truth test item, and compute the metrics based on this set.
Regarding the Implementation Details and Baselines, please refer to Appendix E.2 and E.3.
4.2 PERFORMANCE COMPARISON (RQ1)
Table I shows the performance comparison between I-LLMRec and baselines across four datasets. The key observations are as follows: 1) L.LM-hased models generally outperform traditional CF models, highlighting the effectiveness of leveraging an L.M backbone for the recommender sys- tems. 2) A-LLMRec and TRSR generally outperform TALLRec. It suggests that beyond the at- tributes, incorporating CF item embeddings or item descriptions further enhances the LLM's ability to capture user preferences. However, we observe that A-LLMRec performs worse the Phone dataset, and TRSR generally outperforms A-LLMRec, indicating that item descriptions are more effective than ID-based item embeddings in expressing item semantics. 3) I-LLMRec outperforms UniMP, an image-aware LLM-based model. Given that UniMP employs a multimodal LLM where images and texts are pre-aligned, it indicates the need for more specialized alignment strategy tai- lored to the recommendation tasks, demonstrating the effectiveness of I-LLMRec. 4) Considering that TRSR includes attributes during summarization (See Appendix A), adding description infor- mation in RALLRec, which is equivalent to TRSR, improved performance, whereas descriptions in addition to images (i.e.. I-LLMRec+D*) did not improve the performance of I-LLMRec. This suggests that while descriptions convey useful information, they are inherently limited by what is already present in images due to the significant information overlap between image and description
PI-LLMRec+D D is variant of I-LLMRec that represent items using hoth image features and text description.
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pairs. This supports the idea that item images sufficient to capture user preferences for recom- mendation. For further experimental results on additional datasets, such as visual-centric datasets, please refer to Appendix E.4.
4.3 MODEL ANALYSIS (RQ2)
Analysis on Efficiency. To study the
Amazon Sport
Amazon
efficiency of I-LLMRec, we evaluate the
inference time across different sizes of user's item interaction sequences (i.e.. S.
Inference Time
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Specifically, we divided the users into groups according to Sy,], randomly selected 100 users per group, and measured their total inference time. As shown in Figure 3. ob- serve that TRSR and I-LLMRec+D, which rely on lengthy descriptions, show sharp increase in inference time as the length of textual descriptions within the prompt increases propor- tional to (.,). On the other hand, I-LLMRec maintains consistently low inference time regardless of [S This efficiency stems from the fact that even as |S,]] increases, only minimal number of added, keeping computational costs low. Furthermore, which is an attribute- based representation approach, shows a gradual increase in inference time and eventually surpasses I-LLMRec when [S,. 2 10. We attribute this to the fact that processing natural language attributes scales in complexity faster than image processing required for I-LLMRec. In summary, I-LLMRec is more efficient than TRSR across all S,, and even more efficient than TALL- Rec when user interactions are high, demonstrating superior computational efficiency. Please note that this efficiency analysis includes the pre-computation of visual features, in line with real-world recommendation systems. For detailed discussion on pre-computation, refer to the Appendix. E.5.
0-7
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Users' Itom Interaction Sequence longth ([5,]) User's Itom intoraccionnnnncoo Soquenoc longth (,5.D
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image tokens
observe that TALLRec,
Analysis on Effectiveness. To gain deeper understanding of the effectiveness of I- LLMRec, we examine how well it performs un- der limited budget compared language-basee approach. Specifically, we con- sider the LLM's context window size (i.e., max- imum input token length) as the budget, and vary it from 4,096 256 tokens. This allows us to analyze the ability of models in effectively capturing the user preferences given limited budget. As shown in Figure 4, we observe that as the context window size drops below 512, the performance of TRSR and I-LLMRec+D declines sharply. This is because lengthy descrip- tions make it difficult for the LLM to fully process interactions with limited context window size, thereby making more challenging to understand user preferences. On the other hand, I-LLMRec maintains stable performance even at the context window size of 256 by representing item semantics using minimal image tokens. This enables the LLM to process full user interactions within the given budget and effectively capture user preferences. While TALLRec also remains stable at 256 due to its low token usage, exhibits inferior performance because its attributes are not expressive enough. Overall, I-LLMRec effectively overcomes the context window size constraints by leveraging item images preserve rich item semantics, demonstrating its effectiveness and practicality.
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Figure 4: Performance of various LLM context window sizes.
Analysis on Robustness. To evaluate the robustness of representing items using im- ages instead of natural language as done in I-LLMRec, we compare its performance with various natural language-based ap- Troaches-TALLRes TALLRec (Attribute), TRSR (Summarized description), and Full descrip- acr-as multiple datasets. Figure presents two key findings regarding the draw- back of natural language-based approaches. 1) Information loss from summarization
TALl
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(b) Naise Reduction
Figure 5: Performance of natural language-based approaches (TALLRec, TRSR and Full Descrip- tion) and I-LLMRec on four datasets.
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Amazon Sport
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Figure 6: Performance across item groups, where higher IDs indicate warmer item groups.
(Figure 5(a)): Full description outperforms TRSR, indicating that summarizing descriptions introduces information loss, where essential semantic details may be omitted, leading lower performance. 2) Presence of Noise in Full Description (Figure 5(b)): On some datasets (i.e., Phone and Art), Full description rather performs worse than TRSR that relies on summarized descrip- tion. This indicates that the lengthy full description may contain noise and that the noise could be partially removed in the summarized version of the full description Such different behaviors of natural language-basse
approaches across different datasets demonstrates that these approaches sensitive to the given text, and thus impractical in reality. In contrast, I-LLMRec, consistently delivers strong performance across all datasets, demonstrating its robustness in handling variations in item descriptions.
4.4 ABLATION STUDIES (RQ3)
In Table 2, we conduct ablation studies to un- derstand the effectiveness of each component in I-LMMec. The variant without any additional components is trained solely using Equation I and recommends items based only on image fea- tures (row (a)). We make the following observa- tions: 1) Effect of RISA: We observe that equipment with the RISA module significantly improves performance (row (a) vs. row (b)). This indicates that the RISA module effectively aligns visual features with the language space, enhancing the LLM's ability to understand user preferences. 2) Effect of extending to multiple feature types in RERI: We observe that using two features to- gether"! yields better performance than using either the image or CF feature alone (row (b), (c) vs. (d)). Moreover, integrating all three feature types (i.e., Image, CF, and Text) further improves performance (row (d) vs. (e)). This demonstrates that extending multiple feature types enhances recommendation effectiveness beyond image features alone.
Table 2: Ablation studies of I-LMMRec.
RISA
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0.3953
0.3941
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0,3915
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0A116
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0.644)
0.4811
0.5630
0.3995
0,2766
0.477
0.7711
0,588
0.6879
To further analyze the impact of different feature types, we evaluate their performance on cold and warm item recommendations. Specifically, we follow (Liu et al., 2023b) by sorting the items in ascending order based on the frequency of interaction and dividing them into five equally sized groups, where a higher group ID indicates warmer item group, Then, we compare the recommendation performance across these groups As shown Figure 6, we observe that using image features (blue bar) generally outperforms CF features (orange bar) for cold items (lower ID groups). However, as the item group transitions from cold warm, CF's effectiveness improves, eventually surpassing Image in Item Group 5 (Warm). This suggests that CF is particularly effective for recommending warm items. With these insights, combining Image and CF (green bar) helps mitigate CF's weakness in recommending cold items, resulting in higher performance than CF alone in Item Group 1-2. At the same time, the CF features compensate for weakness of the image features in recommending warm items, leading better performance than image alone in Item Group 4-5. Furthermore, adding textual features (red bar) further boosts performance across all Item Groups, exceeding the performance of Image+CR. This analysis highlights how different feature types complement each other to enhance recommendation.
Effectiveness of Feature types across cold/warm items.
DISCUSSION
Rethinking Information Overlap. We note that the observation of information overlap between image and text features, as discussed in Section 1, relates to the challenges of redundancy in mul-
10 Indeed, we found that full descriptions often contain extraneous content, such as HTML tags introduced during data crawling.
The inclusion of specific feature types applied both in model training (Fquation

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timodal recommender systems (Zhou et al., 2023; Yang et al., 2025; Jeong al., 2024). Here, discuss how this information overlap has been perceived previous studies and articulate how our viewpoint departs from these interpretations. Specifically, prior studies on multimodal recommender systems generally view this information overlap as redundant information that seen as a barrier to improving performance when both modalities are used. Therefore, they generally aim to extract complementary information from each modality to improve performance. On the other hand, we present fundamentally different viewpoint, asserting that this information overlap is in fact cru- cial factor addressing the trade-off between efficiency and effectiveness when representing items for LLMs in natural language. In other words, we view this information overlap as an advantage for LLM-based recommender systems rather than a barrier. Moreover, this perspective allows us to maintain robust performance in real-world cases where item images are absent, as detailed in Appendix E.8 (RQ4).
RELATED WORK
LLM-based Recommendation. A myriad of studies have spurred the development of LLMs for recommender systems (Bao et al., 2023; Wang & Lim, 2023; Zhang et al., 2023; Liu et al., 2024b). Prior works adapt LLMs to recommendation by representing interaction history as item title se- quences and enhancing performance with item attributes to enrich semantics. (Attribute-based Representation). Specifically, TALLRec (Bao al., 2023) converts item IDs into their titles and captures user preference via LoRA (Hu et al., 2021) fine-tuning. TransRec (Lin et al., 2024b) im- proves the user preference understanding by including item titles and attributes in prompts. IDGen- Rec (Tan et al., 2024) incorporates item titles generated by an independent LLM for unique and meaningful semantics. More recently, to feed the richer item semantics to LLMs, description of items have been utilized DDscriptionnbasee Representation). Specifically, TRSR (Zheng et al., 2024b) utilizes large LLM (i.e., LLaMA-3000 30B-instruct (Touvron et al., 2023a)) summarize item descriptions, and feeds them to a smaller LLM (i.e., LLaMA-2 7B (Touvron et al., 2023b)) to cap- ture the condensed meaningful information, while ONCE (Liu et al., 2024b) adopts GPT-3.5 (Brown et al., 2020) for the summarizing task. Despite their notable success to capture the richer item seman- tics, they face trade-off between efficiency and effectiveness when representing items in natural language, leading us to utilize image information to address this trade-off.
Please see Appendix F for additional related works (c.g., multimodal LLM- recommendation).
7 CONCLUSION
In this work, we address the trade-off between efficiency and effectiveness when representing item semantics in natural language for an LLM-based recommender system. Based on the observation that there exists significant information overlap between images and descriptions associated with items, we propose I-LLMRec, a novel method that leverages images to capture rich item seman- ties of lengthy descriptions with only few tokens, thereby capturing user preferences efficiently and effectively. However, as the image and language space are not inherently aligned, we propose the Recommendatiion-orientee Image-LLM Semantic Alignment (RISA) module, which effectively bridges the gap between these spaces. Furthermore, we propose the REtrieval-based Recommen- dation via Image features (RERI) module, a retrieval-based recommendation approach, to enhance both the reliability and efficiency of the recommender systems. Our extensive experiments demon- strate that I-LLMRec outperforms natural language- based approaches in terms of both efficiency and effectiveness. Regarding the limitation and future work, please refer Appendix G
ACKNOWLEDDEEENN
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea governmen((MSIT) (RS-2022-I122)) Robust, Fair, Extensible Data-Centric Continual Learning) as well as another IITP grant funded by the Korea govern- ment(MSIT) (RS-2022-11220077 Reasoning, and Inference from Heterogeneous Data).
ETHICS STATEMENT
In accordance with the ICLR Code of Ethics, to the best of our knowledge, we have not encountered any ethical concerns in the course of this work. Furthermore, all datasets and pre-trained models used in our experiments are publicly available.
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REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Iige Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 2023.
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre. Shiori Sagawa, al. Openflamingo: An open- source framework for training large autoregressive vision-language models. arXiv preprint arXiv:2308. 3900, 2023.
Keqin Bao, Jizhi Zhang. Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1007-1014, 2023.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Joya Chen, Zhaoyang Lv, Shiwei Wu, Kevin Qinghong Lin, Chenan Song, Difei Gao, Jii Liu, Ziteng Gao, Dongxing Mao, and Mike Zheng Shou. Videollm-online: Online video large language model for streaming video. In Proceedings of the IEEEJCVF Conference on Computer Vision and Pattern Recognition, pp. 18407-18418, 2024.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* * chatgpt quality, march 2023. URL https://msys. org/blog/2023-03: 30-vicuna, 3(5), 2023.
Ruining He and Julian McAuley. Fusing similarity models with markov chains for sparse sequential recommendation. 2016 IEEE 16th international conference on data mining (ICDM), pp. 191- 200. IEEE, 2016a.
Ruining He and Julian McAuley. Vbpr: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI conference on artificial intelligence, volume 30, 2016b.
Hidasi. Session-based recommendations with recurrent neural networks. arXiv preprint
arXiv:1511.06933 2015.
Yupeng Hou, Jianmo Ni. Zhankui He, Noveen Sachdeva, Wang-Cheng Kang, Ed Chi, Julian McAuley, and Derek Zhiyuan Cheng. Actionpiece: Contextually tokenizing action sequences for generative recommendation. arXiv preprint uxXiv:2502.1358/ 2025.
Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Wang. and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09688, 2021.
Euiju Jeong, Xinzhe Li, Angela Kwon, Seonu Park, Qinglong Li, and Jaekyeong Kim. A multimodal recommender system using deep learning techniques combining review texts and images. Applied Sciences, 14(20):9206, 2024.
Wang-Cheng Kang and Julian McAuley, Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM), pp. 197-206. IEEE, 2018
Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM conference on recommender systems, pp. 233 233-240,22 2016.
Haven Kim, Yupeng Hou, and Julian McAuley. Fusid: Modality- semantic ids for generative music recommendation. arXiv preprint arXiv:2601.0877 2026.
Kibum Kim, Dongmin Hyun, Sukwon Yun, and Chanyoung Park. Melt: Mutual enhancement of long-tailed user and item for sequential recommendation. In Proceedings of the 46th international ACM SIGIR conference Research and development information retrieval, pp. 68 68-77, 2023.

---

Published as a conference paper at ICLR 2026
Kibum Kim, Kanghoon Yoon, Jachyeong Jeon, Yeonjun In, Jinyoung Moon, Donghyun Kim, and Chanyoung Park. LlmAsgg: Large language models for weakly supervised scene graph generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp-
28306-2883 28316, 2024a.
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. Large language models meet collaborative filtering: An efficient all-round llm-based recom- mender system. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1395-1406, 2024b.
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingho Shang. and Julian McAuley. Text all you need: Learning language representations for sequential recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1258- 1267, 2023a.
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. Neural attentive session- based recommendation. In Proceedings the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419-1428, 2017.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-iman image pre- training with frozen image encoders and large language models. In International conference on machine learning. pp. 17730-19742 PMLR, 2023b.
Jiahao Liang, Xiangyu Zhao, Muyang Li, Zijian Zhang, Wanyu Wang, Haochen Liu, and Zitao Liu. Mmmlp: Multi-modal multilayer perceptron for sequential recommendations. In Proceedings of the ACM Web Conference 2023, pp. 1109-1117, 2023.
Guojiao Lin, Meng Zhen, Dongjie Wang, Qingqing Long, Yuanchun Zhou, and Meng Xiao. Gume: Graphs and user modalities enhancement for long-tail multimodal recommendation. In Proceed- ings of the 33rd ACM International Conference on Information and Knowledge Management, pp- 1400-1409, 2024a.
Tsung-Y Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Pro- ceedings, Part V 13, pp. 740-755. Springer, 2014.
Xinyu Lin, Wenjie Wang. Yongqi Li, Fuli Feng, Sce-Kiong Ng, and Tat-Seng Chua. Bridging items and language: A transition paradigm for large language model-based recommendation. In Pro- ceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1816-1826, 2024b.
Xinyu Lin, Haihan Shi, Wenjie Wang, Fuli Feng, Qifan Wang, See-Kiong Ng, and Tat-Seng Chua. Order-agnostic identifier for large language model-based generative recommendation. In Pro- ceedings of the 48th international ACM SIGIR conference on reseurch and development in infor- mation retrieval, pp. 1923-1933, 2025.
Carlos García Ling, ElizabethHMMrroup FridaRim, inversion, Jaime Ferrando, Maggie, neuralover- flow, and xlsrIn. H&m personalized fashion recommendations. https: waggle. com/ competition ienetiiiioss/tt and-m http://aaaanmmepesaaaoaaeeeeee fashion oe Kaggle.
2022.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892-34916 2023a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024a.
Jingzhe Liu, Liam Collins, Jiliang Tang, Tong Zhao, Neil Shah, and Clark Mingxuan Ju. Under- standing generative recommendation with semantic ids from model-scaling view. arXiv preprint 2rXiv:2509.25522 2025.
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. Once: Elevating content-based recom- mendation with both open-and closed-source large language models. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 452-461, 2024b.
12

---

Published as a conference paper at ICLR 2026
Taichi Liu, Chen Gao, Zhenyu Wang, Dong Li, Jianye Hao, Depeng Jin, and Yong Li. Uncertainty- aware consistency learning for cold-start item recommendation. In Proceedings of the 46th In- lernational ACM SIGIR Conference Research and Development in Information Retrieval, pp- 2466-2470, 2023b.
Tariq Mahmood and Francesco Ricci. Learning and adaptivity in interactive recommender systems. In Proceedings of the ninth international conference on Electronic commerce, pp. 75-774--44 84, 2007.
Jianmo Ni. Jiacheng Li, and Julian McAuley. Justifying recommendations using distanty-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-UJCNL)), pp. 188-197, 2019.
Yongxin Ni, Yu Cheng. Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, and Fajie Yuan. A content-driven micro-video recommendation dataset at scale. In Proceedings of the 34th ACM International Conference Information and Knowledge Management, pp. 6486- 6491, 2025.
OpenAI. Gpt-4o mini. https//wwww.openai.com 2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp- PMLR, 2021.
Shashank Rajput, Nikhil Mchta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong. Yi Tay, Vinh Tran, Jonah Samost, al. Recommender systems with gener- ative retrieval. Advances in Neural Information Processing Systems, 36: 16:10099-- 2023,
N Reimers. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv: 10084, 2019.
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:/205.200 2012.
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. Bert4rec: Sequen- tial recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management, pp. 1441- 1450, 2019.
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge. Zelong Li, and Yongfeng Zhang. Idgenrec: Llm-recsys alignment with textual id learning. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 355-3644 2024.
Jiaxi Tang and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining, pp. 565-573, 2018
Yu Tian, Jianxin Chang, Yanan Niu, Yang Song, and Chenliang Li. When multi-level meets multi- interest: A multi-grained neural model for sequential recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp. 1632-1641, 2022.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: open and efficient foundation language models. arxiv. arXiv preprint arXiv:2302. /3971, 2023a.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXh::2307.09288 2023b.
A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017.
Lei Wang and Ee-Peng Lim. Zero-shot next-item recommendation using large pretrained language models. arXiv preprint 23 2023.
13

---

Published as a conference paper at ICLR 2026
Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR conference on Reseurch and Development in Information Retrieval, pp. 402-412,22 2015.
Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, al. Towards unified multi-modal personalization: Large vision-language models for generative recommendation and beyond. arXiv preprini arXiv:2403. 10667, 2024.
Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li, and Yongfeng Huang. Is news recommendation sequential recommendation task? In Proceedings of the 45th international ACM SIGIR conference on research and development information retrieval, pp. 2382-2386, 2022.
Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, and Danqi Chen. Sheared llama: Accelerating language model pre-trate pre-training via structured pruning. arXiv preprint arXiv:2310.06694, 2023.
Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. Travelplanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622, 2024.
Yueqi Xie, Peilin Zhou, and Sunghun Kim. Decoupled side information fusion for sequential rec- ommendation. In Proceedings of the 45th international ACM SIGIR conference research and development in information retrieval, pp. 1611-162) 2022.
Chengfeng Xu, Jian Feng, Pengpeng Zhao, Fuzhen Zhuang, Deqing Wang, Yanchi Liu, and Victor S Sheng. Long-and short-term self-attention network for sequential recommendation, Neurocom- puting, 423:580-588 589, 2021.
Jinfeng Xu, Zheyu Chen, Wei Wang, Xiping Hu, Sang- Bang-Wook Kim, and Edith CH Ngai. Cohesion: Composite graph convolutional network with dual-stage fusion for multimodal recommendation. In Proceedings the 48th International ACM SIGIR Conference Research and Development in Information Retrieval, pp. 1830-1839, 2025.
Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia, Zhanhui Kang, and Leyu Lin. Improving multi-modas recommender systems by denoising and aligning multi-modal content and user feedback. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3645-3656, 2024.
Jia-Qi Yang, Chenglei Dai, Dan Ou, Dongshuai Li, Ju Huang, De-Chuan Zhan, Xiaoyi Zeng, and Yang Yang. Courier: contrastive user intention reconstruction for large-scale visual recommenda- tion. Frontiers of Computer Science, 1977), 2025.
Yuyang Ye, Zhi Zheng, Yishan Shen, Tianshu Wang, Hengruo Zhang, Peijun Zhu, Runlong Yu, Kai Zhang, and Hui Xiong. Harnessing multimodal large language models for multimodal sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 13069-13077, 2025.
Fajic Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. A simple convolutional generative network for next item recommendation. In Proceedings the twelfth ACM international conference on web search and data mining, pp. 582-590, 2019.
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. Sound- stream: An end-to-end neural audio codec. IEFF/ACM Transactions Audio, Speech, and Lan- guage Processing, 30:495-507, 2021.
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEFF/VV International Conference on Computer Vision, pp. 11975-11986, 2023.
Yang Zhang, Fuli Feng. Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. Collm: Integrat- ing collaborative embeddings into large language models for recommendation, arXiv preprint arXiv:2310. T.../945 19488, 2023.
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Published as a conference paper at ICLR 2026
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, and Ji Ji-Rong Wen. Adapting large language models by integrating collaborative semanties for recommendation. In 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 1435-1448. IEEE, 2024a.
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. Harnessing large language models for text-rich sequential recommendation. In Proceedings of the ACM on Web Conference 2024, pp. 3207-3216, 2024b.
Hongyu Zhou, Xin Zhou, Lingzi Zhang, and Zhiqi Shen. Enhancing dyadic relations with homoge- neous graphs for multimodal recommendation. arXiv preprint arXiv:2301.12097 2023.
Peilin Zhou, Chao Liu, Jing Ren, Xinfeng Zhou, Yueqi Xie, Meng Cao, Zhongtao Rao, You-Liang Huang, Dading Chong, Junling Liu, al. When large vision language models meet multimodal sequential recommendation: An empirical study. In Proceedings of the ACM on Web Conference 2025, pp. 292, 2025.
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Collaborative large language model for recommender systems. In Proceedings of the ACM on Web Conference 2024, Pp- 3162-3172, 2024.
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Supplementary Material
Tloken-Efficier Item Representation via Images for LLM Recommender Systems-
Prompt for Summarization
17
Recommendation Protocol
17
Consistent Observation of Information Overlap
18
D Proposed Method: I-LLMRec
18
D.I RISA Prompt Templates
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D.2 Discussion of LLM-finetuingg for I-LLMRec
19
D.3 Discussion and Comparison with Semantic ID) and Textual Identifier Approaches
19
Experiments
20
E.1 Data Statistics
20
E.2
Implementation Details
20
E.3
Baselines
20
E.4
Analysis on Other Datasets
E.5
Regarding the Precomputation of Visual Features
22
E.6
Effectiveness of Image Features on Unseen Items
................................
22
E.7
Effectiveness of Feature Types Across Cold/Warm Users
................................
23
E.8
Missing Image Scenario
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E,9
Exploring Sensitivity to Image Quality
23
E.
Training Strategy
24
E.11
Case Study the Impact of Figure Types
24
E.12 Impact of Different LLMs and Vision Encoders
25
E. Simulation of Scaling Item Catalog
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Additional Related Works
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G Limitation and Future work
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Given the item's metadata, including the title, attributes, and description, craft concise summary:
Title: (TITLE)
Brand: (BRAND)
Category: (CATEGORY)
Description: (DESCRIPTION)
Answer:
Figure 7: Prompt for summarizing descriptions. The red-colored text denotes the actual metadata for the item.
A PROMPT FOR SUMMARIZATION
Figure 7 shows the prompt used for description summarization. Following TRSR Zheng et al. (2024b), we incorporate attribute information along with the description to generate a concise sum- mary.
B RECOMMENDATION PROTOCOL
In this section, we describe a comprehensive explanation of the recommendation protocol, which is consistently applied to suribute-basse Representation, Description-baut based Representation (i.e., Full Description and Summarized Description), and I-LLMRec introduced in Section 1. Note that this recommendation protocol follows a retrieval-bes based recommendation approach similar to the RERI module discussed in Section 3.3. Therefore, we recommend reading Section 3.3 first for a deeper understanding. At a high level, we modify the way items are represented by replacing the visual representation with either an attribute-based or description-base representation within P,, followed by recommending process similar to RERI module.
Specifically, first craft the prompt, which includes the task prompt (as shown in Figure 2) and the semantics of isserinteracted items in a sequence. Here, the item semantics depend on the item representation approach, which plays crucial role in capturing user preferences. Then, append learnable token [REC] at the end, allowing this token to aggregate the semantics of items the user has interacted with, thereby capturing user preferences. To obtain the corresponding representation, we use the hidden state of [REC] token, which serves as the LLM-guided user representation. Using this representation, we retrieve relevant items by comparing with items' visual, collaborative filtering (CF), and textual features. More precisely, for each feature type, we compute affinity scores by performing dot-product between the -Lgdddd user representation and item features. Based on the summation of affinity score across feature types for all items, we rank the items and recommend the Top-k items with the highest scores.
2000
Amazon Phone (Image Description)
Frequency
Grocery limage Description)
1500
[Image Positive Caption)
coCO [Image-Xegative Caption)
1000
500
0
0.1
0.0
0.1
0.2
0.3
0.4
0.5
Similarity Score
Figure 8: Cosine similarity between item image-description pairs in Amazon Phone and Grocery datasets and image-caption pairs in the COCO dataset using CLIP Radford et al. (2021).
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Sport
Art
Grocery
Phone
2000
Frequency
10000
1500
8000
1500
6900
3000
1000
2000
500
500
1000
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Similarity Score
Similarity Score
Similarity Score
Similarity Score
Figure 9: Sigmoid- based similarity (ranging from O to 1) between item image-description pairs across four datasets (Amazon Sport, Art, Grocery, and Phone datasets) using SigLIP Zhai et al. (2023).
C CONSISTENT OBSERVATION OF INFORMATION OVERLAP
We further investigate the information overlap between image and description associated with items, which explored in Section I, by expanding these experiments additional datasets and leverag- ing another CLIP-based model. 1) Expanding to additional datasets: Building upon our previous comparative experiments in Amazon Sport and Art-where used CLIP Radford et al. (2021) to measure cosine similarity Section 1 I-we we extend our analysis to the Amazon Grocery and Phone datasets. As shown in Figure 8, the average similarity for Amazon Phone and Grocery is approxi- mately 0.31, which higher than 0.26 average similarity observed in well well-aligned COCO image- caption pairs. This consistent observation across multiple datasets further supports the information overlap between item images and descriptions. 2) Leveraging another CLIP model: To further explore the information overlap using different CLIP variant, we select SigLIP Zhai et al. (2023), a variant of CLIP with sigmoid loss function, to compute the similarity scores ranging from 0 to 1. Specifically, when we apply SigLIP to measure similarity between item mage-description pairs across all four datasets (Amazon Sport, Art, Grocery, and Phone), we observed that, as show in Fig- ure 9, the majority of items exhibit similarity scores close to 1, further demonstrating the significant information overlap between their image and description pairs.
D PROPOSED METHOD: I-LLMREC
Question Templates for
Question Templates Category
"Terplate "Whrt this userg nad
"Terppate th: "Wh product categary the user thep from neat??
"Template di) "Wiut trane the lieely to pick for thei purchase?
"Temploce di) "rron wi category the user's next ner selected??
"Teerpote the sver dedide for their
"Teerpooe d:: "Wiee be categery of purchosa
"Templatt
Template
Template Category
"Jerplon"" bond
conurned
"Teepboy""
neet conouned (CAmGORe?
Question Templates for Title
Question Templates for Description
"Temppore 1") an'ss tite of the nee ter this user plas to tobuy??
Teespöre 1",
Woace desorit the denal the
Pleare decalled deser prion the this usor woult cons men
"Teeppite "Wh
thaly buy
"Tempbaee 2":
irtende contuma."
"Tamplata
"template d") "Wiut the of not product this plans buy??
"tenslite d"
"Deu be fucures of the this lecle conoume""
"Teerpone "Mivet the tite the neet purthase thicureri plaaning
"Tempbrre 27:
Con sooe the quecitrs the and tem this going cnnume??
Answer Template for Title
Answer Template for Description
"Jorplac": thenotiion becoroumeo (mmaaa"
"lumpkte" of Ieem becorummall
[reatums]
Figure 10: Prompt template for RISA module. The red-colored text denotes the actual information of an item.
D.1 RISA PROMPT TEMPLATES
Figure 10 shows the prompt template for RISA module of I-LLMRec. As described in Section 3.2. we utilize the prompt templates to align the visual feature with the language space LLMs by training the adaptor network M.
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D.2 DISCUSSION OF
FOR I-LLMREC
A further benefit of I-LLMRec is that LLM fine-tuning is not mandatory, unlike the existing LLM- based recommender systems (Lin et al., 2024b; Bao et al., 2023; Tan et al., 2024; Zheng et al,, 2024b; Liu et al., 2024b) that generally rely on costly fine-tum tuning of the LLM. Instead, we harness the LLM's 's intrinsic parametric knowledge to understand the user preferences. Throughout this paper, we opt to keep the LLM frozen to preserve the LLM's intrinsic knowledge and improve the training
efficiency.
Potential Improvement through LLM fine-tuning Nevertheless, we explore po- tential performance gains from fine-tuning the LLM, common approach in exist- ing LLM-based recommender systems Lin et al. (2024b); Bao et al. (2023); Tan et al. (2024); Zheng et al. (2024b); Liu et al. (2024b). However, since fully fine-tuning an LLM demands substantial computational resources, we adopt the parameter-efffiient fine-tuning strategy, LoRA Hu et al. (2021), which introduces a small number of learnable rank matrices into each LLM's transformer layer. As shown in Table 3, we observed the performance improvement through fine-tuning, indicating that there is room for further improvement within I-LLMRec. How- ever, considering the computational cost, we opt to freeze the LLM.
Table 3: Performance comparison when LLM back- bone of I-LLMRec is fine-tuned using LoRA Hu et al. (2021).
Spurt
Art
Hin@5
NDOG@S
Hit@s
NDCG@5
L.MM Frozen L.M fine-tuning with LoRA
0.4570
0.5711
0.5883
0.4839
D.3 DISCUSSION AND COMPARISON WITH SEMANTIC ID AND TEXTUAL IDENTIFIER APPROACHES
Discussion with Semantic ID Approaches. Recently, generative recommender systems with se- mantic ID methods (Zheng et al., 2024a; Lin et al., 2025; Rajput et al., 2023; Kim et al., 2026; Hou al., 2025) has been emerged. Here, we discuss the differences between our approach and semantic ID approach, and compare the efficiency and performance.
While both approaches share the goal of efficiently representing items within the LLM's capacity, there is a fundamental difference in how they convey the semantics of item descriptions to the LLM. The Semantic ID approach addresses the challenges of randomly initialized item tokens-wiie inherently fail to capture semantic relations between items (Liu et al., 2025)-br by training a vector- quantized model (Zeghidour et al., 2021) that maps item descriptions to discrete codes. These de- scriptions serve as semantic intermediarics, aiding in the capture of semantic relationships between items (i.e., if two items share similar descriptions, they typically have the same codes), and allowing the L.LM to implicitly capture the semantics of the descriptions. On the other hand, our approach explicitly encodes the semantics of each item's s description using images, which provide a richer and more direct representation for the LLM.
Regarding token efficiency, Semantic ID approaches (Zheng et al., 2024a; Lin et al., 2025) generally require four tokens (codes) per item, whereas our approach uses a single image token that captures the semantics of the description. Importantly, Semantic ID approaches essentially require multiple discrete tokens express cross-item semantic relationships, while our approach conveys the se- mantics of the description with just one image token. Beyond token efficiency, we also compare performance with state-of-the-nn art semantic ID method, LC-Ree (Zheng et al., 2024a), under fair evaluation setting using the same LLM backbone (Xia et al., 2023) on the Sport and Art datasets with full item candidates. I-LLMRec achieves H5 scores of 16.28 the Sport dataset and 21.80 on the Art dataset, compared to 15.86 and 20.41 for LC-Rec, respectively. These results indicate that LLMRec not only offers more efficient item representation but also more effective in representing item semantics to the LLMs.
Discussion with Textual Item Identifier. While Semantic ID approaches rely on latent codes (to- kens) for item representation, the textual identifier methods (e-g.. IDGenRec (Tan et al., 2024)) generally generate a paraphrased version of an item's 's title, derived from its textual description, to serve as its identifier. Similar to Semantic ID approaches discussed earlier, this approach also shares the goal of efficiently representing items. However, the textual identifiers inevitably lose the seman- ties of descriptions since they compress the full item description into shortened title-like phrase. As a result, the state-of-the-art textual identifier method, IDGenRec, achieves only 15.11 Hit@ 5 on the Sport dataset and 17.97 on the Art dataset under the full item candidate setting-pprformann
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that is inferior to I-LLMRec (16.28 and 21.80, respectively) and still below that of the semantic ID method LC-Rec.
Regarding token efficiency, IDGenRec compresses roughly 209 tokens of description into an average of 10.9 tokens per item. On the other hand, I-LLMRec uses single visual token that directly encodes the item's semantic content. This leads to significantly lower computational cost during both training and inference, while still retaining richer semantic information.
E EXPERIMENTS
Table 4: Data statistics after preprocessing.
Dataset
# Users
Items
Interactions
Avg. Len
Sport Grocery
24,665
9,884
179,491
7.28
84,896
25,632
739,628
8.71
Art 14,986
5,801
121,917
8.13
Phone 59,307
19,671
403,194
6.80
E. DATA STATISTICS
Table shows the summary of the data statistics after preprocessing. Our experiments were con- ducted datasets of varying scale, ranging from Grocery, which has a large number of interactions, to Art, which has relatively fewer interactions.
E.2 IMPLEMENTATION DETAILS
We employed Sheared LLaMA 2.7B (Xia al., 2023) the LLM backbone. For the visual encoder V. we adopt SigLIP (Zhai et al., 2023), variant of CLIP (Radford et al., 2021) with a sigmoid loss function, extract visual features. Regarding experiments with different LLMS and visual encoders, please refer to Section 12 We use 2-layer MIP with ReLU activation function for the adaptor M and the intermediate dimension is 512. In RERI module, textual encoder. To ensure consistency across the LLM backbone, we use the same Sheared LLaMA 2.7B for TALLRec, A-LLMRec, and TRSR. Since these models are limited to recommending items only from a narrower item pool (i.c., or 20), we apply the same RERI module for recommenda- tion, which incorporates image, CF, and textual features, allowing them to recommend items from larger pool. This approach is acceptable since our main goal is to evaluate how effectively the LLM captures user preferences through item expression. For TRSR, we use GPT-4o (OpenAI, 2024) to summarize full item descriptions. For UniMP, we follow its original implementation, which utilizes OpenFlamingo-444 Instruct (Awadalla et al., 2023), pre-trained multimodal LLM. Regarding train- ing configurations, we set the learning rate 0.001 and the batch size 32. All models are trained on an NVIDIA GeForce A6000 48GB GPU.
employ SBERT (Reimers, 2019) the
E.3 BASELINES
Baselines. We compare I-LLMRec with the following baselines:
Collaborative Filtering (CF) models.
GRU4Rec (Hidasi, 2015) employs the RNN network capture sequential user interactions.
VBPR (He & McAuley, 2016b) enhances BPR (Rendle al., 2012) by incorporating image features to improve personalized ranking.
BERT4Rec (Sun al., 2019) applies bidirectional transformers with masked item prediction to capture complex user preference.
SASRec uses self-attention mechanisms to capture long-term user preference.
L.MM-based models.
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TALLRec (Bao al., 2023) converts numerical IDs into titles while intentionally adding at- tributes (i.e., brand and category), regarding it as a representative model of the Attribute-based Representation approach.
A-LLMRec (Kim et al., 2024b) express items by projecting the CF item embeddings into the L.M along with title, enabling effective recommendation of warm items.
TRSR (Zheng et al., 2024b) is representative work of the Description- based Representation approach, which summarizes full item descriptions effectively express items while leveraging rich textual semantics.
Image-aware LLMM-bssed models.
* UniMP (Wei et al., 2024) utilizes image features and attributes to represent items, and recom- mends them through image tokens assigned as vut-of-vocabulary
I-LLMRec+D (equiv. to ILLMRRc+TRSR)" a variant of I-LLMRec that represent items using both image features and text description.
Note that the main difference between LLM-based models and I-LLMRec lies in how item seman- ties are represented (i.e., attributes, CF, descriptions, or images) in that both share the same LLM backbone and recommendation protocol.
Table 5: Performance results on three additional Amazon datasets (Automotive, Video, and Cloth- ing) and the H&M Fashion dataset, with cosine similarity (Cosine Sim.) between item images and descriptions measured using CLIP (Radford et al., 2021) is measured.
Automotive (Cosine Sim.: 0.3086)
Video
Clothing (Cosine Sim.: 0.3107)
H&M Fashion
Model
(Cosine Sim.: 0.2801)
(Cosine Sim.: 0,2847)
Hit@5
NDCG@5
Hit@5
NDCG@5
Hit@5
NDCG@5
Hit@5
NDCG@5
SASRec
0.4027
0.3078
0.6045
0.4713
0.4981
0.4690
0.4918
0.3885
TALLRcc
0.4203
0.3107
0.6040
0.4521
0.4388
0.3660
0.3959
0,2827
I-LLMRcc
0.4515
0.3379
0.6413
0.4915
0,5469
0.4927
0.5321
0,3949
E.4 ANALYSIS ON OTHER DATASETS
visual-centric datasets (Amazon Cloth- ing and H&M Fashion datasets), where users' choices are likely influenced by visual attributes, and two less visual-centric datasets (Amazon Automotive and Video) to examine how I-LLMRec per- forms in visual-centric datasets compared to baselines, including the collaborative filtering model (SASRec(Kang & McAuley, 2018)) and natural language-based LLM approach (TALLRec (Bao et al., 2023)). As shown in Table 5, we observe that TALLRec performs on par with SASRec on the less visual-centric datasets, while showing a significant performance drop visual-centric ones. indicates that natural language alone is insufficient to represent items for LLMs in vision-driven contexts, making it challenging to capture user preferences driven by visual attributes. I-LLMRec, however, effectively leverages image information, yielding significantly higher performance than TALLRec, especially on visual-centric datasets. These results highlight the particular effectiveness of I-LLMRec in vision-driven contexts, while its solid performance on Automotive and Video fur- ther demonstrate its general effectiveness.
Behavior visual-centric datasets. We evaluate
Generalization of the observation. To further demonstrate the information overlap between item images and descriptions discussed in Section 1, we measured the cosine similarity between images and descriptions on three additional Amazon datasets (Automotive, Video, and Clothing) as well as the H&M Fashion dataset (Ling et al., 2022) from another domain. As shown in Table 5, the high cosine similarity (Cosine Sim.) reported under each dataset consistently reveals strong semantic overlap between images and descriptions, alongside superior performance of I-LLMRec over the baselines. While we could not cover every real-world dataset, the consistent results across eight the four the main paper-demonstre the general applicability of informa- tion overlap, suggesting that I-LLMRec can be broadly applied.
For each item in the user interaction, append the description "Description: D," after P. within the prompt, i.c., P, (Title: ITEM TITLE, Visual Representation [VISUAL], Description D,
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Generalization to the Video Domain. We extended our eval- uation to the micro-video recommendation domain by using the MicroLens (Ni et al., 2025) dataset and compared the per- formance with SASRec and TALLRec baselines. For visual features v, we average the image features of sampled frames provided by the Microlens dataset. The results, as shown in the Table 6, show that TALLRec performs similarly to a tra- ditional collaborative filtering model (SASRec), highlighting that user preferences based solely on titles are insufficient, On the other hand, I-LLMRec signifi- cantly outperforms TALLRec, demonstrating the generalization of I-LLMRec to the video domain.
Table 6: Performance on Microl.ens dataset.
Hit@s
NDCG@5
SASRec
0.4567
0.3413
TALLRec
0.4459
0.3237
I-LLMRec
0.4854
0.3520
Behavior on Visually- Weak Domains. To explore the be- havior of I-LLMRec on visually weak domains, we conducted additional experiments on the Goodbooks dataset with mul- tiple baselines (SASRec, TALLRec, and TRSR). As shown in Table 7, the description-baset model TRSR outperforms I- LLMRec on this dataset. This aligns with intuition: visu- ally weak domains, visual features carry limited information for capturing user preferences, and textual descriptions nat- urally become more informative. However, we highlight two important points. First, while long textual descriptions can help in fully text-driven domains like Goodbooks, relying on them is often impractical due to significant computational cost. For example, TRSR incurs 2.8x longer inference time and 3.7x longer training time than I-LLMRec on Good- books. Furthermore, although we can leverage descriptions in such domains due to their low im- age-description cosine similarity (0.16 compared over 0.29 in other datasets), these domains are limited in scope. Second, across the eight datasets used in the paper-includinn Sport and Phone, which can also be considered visually weak-we consistently observe the high degree of semantic overlap between item images and descriptions, and I-LLMRéc performs effectively, demonstrating that I-LLMRec generalizes beyond purely visually rich domains (e.g.. Fashion). In summary, the efficiency and effectiveness of I-LLMRec across diverse domains make it broadly more suitable for real-world recommendation scenarios.
Table 7: Performance Good- books dataset.
Hit@5
NDCG@5
SASRec
0.4475
0.2956
TALLRec
0.4752
0.3266
TRSR
0,5100
0.3551
I-LLMRcc
0.4870
0.3372
E.5 REGARDING THE PRECOMPUTATION OF VISUAL FEATURES
Here, we clarify how visual feature extraction is handled in our efficiency evaluation. Following prior multimodal recommendation studies (Xv et al., 2024; Xu et al., 2025; Lin et al., 2024a), we pre-compute and cache all item visual features before both training and inference. Thus, the wall- clock times in Figure 3 of the main paper exclude visual feature extraction cost. However, we argue that this setup aligns with real-world recommendation scenarios: once an item's visual features are extracted offline, the same representation during both training and inference. Prior work (Xv et al., 2024; Xu et al., 2025) adopts the same con- vention when evaluating efficiency. Furthermore, it is important note that caching these features is inexpensive-onl only 3.4KB per item, making the memory requirement extremely lightweight.
be reused for every user who interacts with that item,
To conduct a thorough comparison of inference time with the description-basel baseline (TRSR), measure the inference time including visual feature extraction. On the Sports dataset, TRSR takes approximately 47 minutes for recommendation inference, while I-LLMRec requires 15 minutes for recommendation inference and additional 5 minutes for visual feature extraction, totaling around 20 minutes. Even with this setup, I-LLMRec is 2.4 times more efficient.
E.6 EFFECTIVENESS OF IMAGE FEATURES ON UNSEEN ITEMS
Extending the analysis of cold-start items in Section 4.4 of the main paper, to further demonstrate the effectiveness of image features for cold-start items, we evaluate the Sports and Art datasets by select- ing users whose test items do not appear in the training set (i.c., extreme cold-start case), resulting in 302 users in Sports and 29 in Art. In this setting, using only CF features in the RERI module leads to near-zero performance, since unseen items retain untrained initial embeddings. On the other
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hand, image features achieve @ of 0.3245 on Sports and 0.0689 Art, demonstrating their effectiveness in recommending extreme cold start items.
Amazon Sport
Amazon Art
0.5
0.4
Hit
0.5
Higg
Prage (ow Iragee
prage New
0.1
BrageeCk Hoo in
(Caks)
(Nkrm))
(Wbmm)
Figure 11: Performance across user groups, where higher IDs indicate warmer user groups.
E.7 EFFECTIVENESS OF FEATURE TYPES ACROSS COLD/WARM USERS
We examine how different feature types affect performance under both cold- and warm-user rec- ommendation settings. Following the same protocol as in Appendix 4.4 of the main paper, we par- tition users into groups based on their sequence length [Sa], ensuring that each group contains the same number of users. As shown in Figure 11, image features (blue bars) consistently outperform CF features across all user groups, indicating that image features remain effective regardless of the user's interaction history length. Moreover, incorporating more feature types progressing from Im- age+CF (green bar) to TTage+CT+Text (red barl-yields steady performance gains in every group, demonstrating that leveraging the full set of features is broadly beneficial. Interestingly, we observe a sequence length increases, which contrasts with trends commonly observed in traditional CF models (Kim et al., 2023). We hypothesize that this decline arises from the difficulty LLMs face in capturing user preferences when longer histories in- troduce greater diversity in the categories of interacted items. Addressing this challenge- accengagcccurtllly modeling user preferences interaction histories grow-remaine important direction for future work.
gradual performance drop across all feature types
E.8 MISSING IMAGE SCENARIO
In this section, we explore how to handle scenarios where item images are missing and evaluate how effectively we can man- age them. Specifically, we randomly re- move 50% of the item images from the en- tire item pool. As a result, these missing im- ages cannot be used for item semantics rep- resentation (P;) and retrieval-bai based recommendation (Equation I and rech the main paper). To compensate for the missing images, we substitute them with readily available textual descriptions. Similarly, for retrieval- features derived from descriptions using SigLIP (Zhai et al., 2023) text encoder rather than extracting visual features from images using SigLIP visual encoder. As shown in Table 8, I-LLMRec trained with half of the items missing images outperforms the baselines (TALLRec and TRSR), even though the baselines were trained without any missing item images. This demonstrate the effectiveness of I- LLMRec in handling the missing image scenario, which shows the practicality of I-LLMRec in reality.
Table 8: Performance when item images are missing.
Sport
Art
Hit8
NDCGe5
Hite5
NDCG85
TALLRcc TRSR
0.3375
0.5841
0.4758
HLLMRoc missing images
0.4451
0.3589
0.5805
0.4789
recommendations, when item images
missing,
extract textual
E.9 EXPLORING SENSITIVITY TO IMAGE QUALITY
Throughout the paper, we use high- resolution images, i.e., high-quality item images. However, in the real-world appli- cations, it may not always be feasible to obtain such high-quality item images, and systems may instead rely on low-quality ones. To examine this issue, investigate how I-LLMRec performs when the low- quality images are used. Specifically, we
Table Performance under low luw-quality images.
Row
Level of Image Quality
Hites
NDCG@5
Using only image features
I-LLMRec w/ low quality images
0.3704
0.2936
(b)
I-LLMRec w/ high quality images
0.4316
0.3403
Using all features (Image, CE Text)
(c)
I-LLMRec w/ low quality images
0.4459
0.3597
I-LLMRec w/ high images
0.4570
0.3711
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replace nigh-resolution images with low-resolution versions originally provided in the metadata. We evaluate this setting on the Amazon Sport dataset under two conditions: (i) using only image features in both training and inference to isolate the effect of image quality, and (ii) using all fea- tures (image, text, and CF). As shown Table 9, we observe that performance drops significantly when relying solely on image features (row (a) vs. (b)), while the model remained robust when other features were included (row (c) vs. (d)). These observations indicate that while I-LLMRec is sen- sitive to image quality when used in isolation, it can still effectively handle low-quality images by leveraging complementary information.
E. TRAINING STRATEGY
Throughout this paper, we optimize our model using an end-to-end training strat- egy to ensure ease of implementation and learning efficiency. However, in this section, we explore the impact of alternative two-stage training strategy, where we separate the RISA and RERI modules. Specifically, for stage I, we train the adaptor M through the RISA module to initially align the visual space with the language space. For stage 2, we freeze the adaptor and train only F and F. (+=[Img, CF, Text]) through the RERI module. As shown in Table 10, the two-stage strategy shows performance comparable to the end-to-end strategy, indicating that neither strategy offers clear advantage over the other. Given this, we opt for the end-to-end training strategy to facilitate the ease of implementation and training efficiency.
Table 10: Training Strategy.
Sport
Art
Training Strategy
Hit@s
NDCG@5
Hit@5
NDCG@5
End-to-End StageI-Stagee
0,4570
0.3711 0.3747
0.5883
0.4839
0.4604
0.5858
0.4887
Positive Set
Negative Set
Sport
Apprte-ppporil tomss
Toctical and Sunvhot rolated itons
Art
Easily Monntiibiot Visca heess
Items Contoining Small Text or Fine Dedoits
Figure 12: Case studies impact of figure types in images.
E. CASE STUDY ON THE IMPACT OF FIGURE TYPES
To study which figure types influence performance, we analyzed the item histories of users who correctly predicted the test item and selected their top 5 most frequently consumed items (positive set), as well as the top items from users who failed to predict the test item correctly (negative set), using the Amazon Sport and Art datasets. We hypothesize that items in the positive set help LLMs capture user preferences, whereas items in the negative set have the opposite effect. As shown in Figure 12, we observe that in the Sport dataset, the positive set mainly consists of sports apparel (e.g., shoe, athletic shirts), while the negative set includes tactical or survival-related gear (e.g., camping pillow, lightbulb). This suggests that the functional category or intended use of an item appears to have an influence on performance. On the other hand, in the Art dataset, items in the positive set typically feature clear and easily recognizable visuals (e.g., paintbrush, yarn), whereas the negative set contains items that require interpreting small text or fine details identify (e.g.. dark chipboard with a small label). These findings indicate that images providing clear visual cues are more beneficial for the model than those requiring detailed or text-based interpretation, instead of the functional category effect observed in the Sport dataset. In conclusion, the types of figures within images that affect performance seem to vary depending on the dataset.
The LLM takes only the item images consumed by input, whereas the retrieval retrieval-based based recommen- dation leverages image, text, and CF features.
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Published as a conference paper at ICLR 2026
E.12 IMPACT OF DIFFERENT LLMS AND VISION ENCODERS
To investigate the impact of the back- bone models the recommendation performance of I-LLMRec. we evalu- ated our model by replacing different backbone models. For the LLM,, we used Sheared LLaMA-2.77 BB Xia et al. (2023) and Vicuna 1. 1.5-7B Chiang et al. (2023), an improved version of LLaMA-7B Touvron et al. (2023a). For the vision encoder, we used CLIPIS Radford et al. (2021) and SigL.IPI6 Zhai et al. (2023), with the advanced version of CLIP scaling with sigmoid loss. Table 11 shows the following observations: 1) Given the same vision encoder, I-LLMRec with Vicuna 1.5-7B outperforms the model with LLaMA-2.7B. This improvement is likely attributed to Vicuna 1.5-7B's superior semantic reasoning capability, allowing it to capture user preferences more effectively. 2) Given the same LLM backbone, I-LLMRec with SigLIP per- forms better than the version with CLIP. This suggests that models that better capture visual features are more effective in delivering item semantics, thereby enabling the LLM to better understand the item semantics. In summary, employing more powerful LLMs and vision encoders leads to improve- ments in recommendation performance, these models effectively capture item semantics and user preference.
Table 11: Impact of the different backbones.
LLM
Vision Eacoder
Sport
Art
Hice5
NDOG@S
Hile5
NDCG@5
LLaMA-2.7B
SigLIP CLIP
0.4570
0.3711
0,5883
0.4839
LLaMA-2.7B
0.4476
0.3560
0,5836
0.4796
Vicuna 1.5-78
SigLIP
0,4670
0.3779
0,5943
0.8930
Vicuna 1.5-7B
CLIP
0:4552
0.3677
0.5885
0.4892
E. SIMULATION OF SCALING ITEM CATALOG
We explore how I-LLMRec manages the scalability of the item catalog by simulating its perfor- mance on the Amazon Phone dataset. In this simulation, we gradually increase the item catalog size from 5K to 20K (the full set). For realistic setting, assume that for each catalog size, there is a corresponding user base that has interacted solely with the items in that catalog. Specifically, we model the following user counts: 2.7K, 13,8K, 33.7K, and 59.3K users for catalog sizes of 5K. 10K, 15K, and 20K, respectively. This approach simulates the growing user base of expanding online services.
The performance of the models trained on each item catalog shows values of 0.468, 0.463, 0.4701, and 0.5106, indicating relatively stable performance even as the catalog size grows. However, we recognize that frequent model retraining results in increased memory and computational costs. To mitigate this, we tested using model trained on smaller catalog to infer for a larger catalog (c.g., model trained on 5K items inferring for 10K, and a model trained on 10K items inferring for 15K). This approach avoids the need for periodic retraining as the catalog size grows and assess how ro- bustly past models can perform the item catalog grows. We observe that the performance remains robust with values of 0.370 (5K model to 10K catalog), 0.422 (10K model to 15K catalog), and 0.444 (15K model to 20K catalog), respectively, thanks to the I-LLMRec's ' cold-start item recom- mendation ability (See Appendix E.5). These results suggest that I-LLMRec can operate efficiently without frequent retraining, significantly reducing both computational complexity and training costs. Regarding system performance, I-LLMRee achieves a throughput of 20 users per second, including the indexing of visual 1 across all catalog sizes. This assumes the use of a fast retrieval model to first retrieve relevant items from the larger pool, with I-LLMRec performing re-ranking within the top 100 candidates. The GPU memory requirements are as follows: 6.7G to load the model, and 21GB of GPU memory during inference with a batch size of 16. In terms of storage, the model weights require 14.3GB, and the visual feature cache requires 3.4KB per item. Thus, for every 5K increase in the catalog size, an additional 17MB of storage is needed. However, as the item catalog grows, the I-LLMRec model weights replace the previous model weights, meaning that no additional storage is required. For cost per request, since LLMdbased recommendation models are primarily used for re-ranking [1], the processing cost remains constant regardless of catalog size, as indexing the visual features is negligible with a complexity of O(1). The key scaling bottleneck is the periodic retraining necessary to maintain performance as the catalog expands. Using a single NVIDIA GeForce A6000 48GB GPU, additional 3 hours of training time is required for every 5K increase in catalog size. However, we anticipate that utilizing multiple GPUs for offline training and fine-tuning models trained on previous catalogs will substantially reduce training time, compared to the current approach, which retrains the model from scratch with each catalog size increase.
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Published as a conference paper at ICLR 2026
F ADDITIONAL RELATED WORKS
Sequential Recommendation, Our setup closely aligns with the sequential recommendation, where user's item interaction history is listed sequence in chronological order, and the goal is to pre- dict the user's next interaction item. Early studies process user sequences via the Markov chain for sequential recommendation (He & McAuley, 2016a; Mahmood & Ricci, 2007; Wang et al., 2015). With the advancement of deep learning. RNNs were used for sequence modeling to capture user preference (Hidasi, 2015; Li et al., 2017), while studies using CNN treat previous interacted items' embedding matrices as images, enabling the convolutional operation to consider user se- quences (Tang & Wang, 2018; Yuan et al., 2019; Kim et al., 2016). Recently, with the emergence of Transformer (Vaswani, 2017), the self-attention mechanism has been applied to sequential recom- mendations (Kang & McAuley, 2018; Sun et al., 2019; Xu et al., 2021). More recently, the focus has shifted towards leveraging rich side information associated with items (e.g., images or text) in the sequential recommendation. Specifically, TempRec (Wu et al., 2022) encodes textual information of items strengthen item embeddings, while MMMLP (Liang al., 2023) fuses visual and textual features in the user sequence to capture fine- fine-grained user preferences.
Multimodal LLM-based Recommendation. Recent studies (Zhou al., 2025; Ye et al., 2025; Wei et al., 2024) have explored leveraging visual information in LLMs for recommendation tasks. Specifically, MSRBench (Zhou et al., 2025) investigates the use of (e.g., GPT-4V (Achiam et al., 2023)) for recommendation various settings, while MLLM-MSR (Ye et al., 2025) and UniMP (Wei et al., 2024) fine-tune the Multimodal LLMs (Awadalla et al., 2023; Liu et al., 2023a) developed Nonetheless, these studies mainly emphasize leveraging Multimodal LLMs to merely boost perfor- mance by feeding image features into LLMs, offering limited analysis of image-description overlap and lacking effective alignment strategies between visual and language spaces tailored to the recom- mendation context.
Multimodal LLMs
computer vision to improve recommendation performance.
G LIMITATION AND FUTURE WORK
In this paper, mainly exploit item images capture user preferences effectively and efficiently for LLMs. A potential limitation of this approach is the possible underutilization of textual infor- mation. Specifically, even if item images sufficient capture semantics, textual descriptions may still provide complementary information such as subtle at- tributes contextual nuances-that information requires carefully disentangling text-specific cues from overlapping visual content and isolating only those parts of text that contribute meaningfully to recommendation tasks, which is challenging and non-trivial process. Although the contribution of this information estimated to be small according to our results in Section 4.2, combining it with image features could further en- hance recommendation performance. We therefore identify this as a promising direction for future research.
preferences by offering rich
not easily conveyed by images. However, extracting such
26


Reviews:
[{"review_id": "OoaLWWDPyR", "reviewer": "Reviewer_xUwd", "summary": "This paper studies the trade-off between efficiency and effectiveness in LLM-based recommendation. Existing methods rely on either attribute-based or description-based item representations, which are limited by low semantics or high token cost. The authors observe strong information overlap between images and descriptions and propose I-LLMRec, which uses images as compact item representations. The model includes two modules: RISA for image–language alignment and RERI for retrieval-based recommendation. Experiments on multiple real-world datasets show that I-LLMRec improves both efficiency and accuracy.", "questions": "1.\tHow does I-LLMRec perform on non-e-commerce datasets, such as MicroLens?\n2.\tCan the authors compare token efficiency with semantic ID approaches such as LC-Rec or SETRec?\n3.\tHow significant is the contribution of the image modality? The ablation study should include the model using CF and text only, without image features.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThe motivation is clear, and the trade-off is well analyzed.\n2.\tThe proposed method is overall reasonable and effective.\n3.\tExperiments are extensive and convincing.\n4.\tThe paper is well organized and easy to follow.", "weaknesses": "1.\tThe paper lacks discussion or comparison with other generative textual item identifier such as IDGenRec [1] and semantic ID methods such as LC-Rec [2] or SETRec [3].\n2.\tThe experiments are limited to Amazon datasets. The generalization to other domains such as micro-video recommendation (e.g., MicroLens) is necessary to strengthen the paper.\n3.\tLack necessary ablation study on the image data, which is a key component of the proposed method. \n\n[1] Tan et al., IDGenRec: LLM-RecSys Alignment with Textual ID Learning. SIGIR’24  \n[2] Zheng et al., Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation. ICDE’24.  \n[3] Lin et al., Order-agnostic Identifier for Large Language Model-based Generative Recommendation. SIGIR’25.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SvO5myoZqK", "reviewer": "Reviewer_3dZW", "summary": "The paper proposes I-LLMRec, an LLM-based recommender that replaces lengthy item descriptions with item images to represent a user’s interaction history. It introduces (1) RISA (Recommendation-oriented Image–LLM Semantic Alignment), which learns an adaptor to map visual features into the LLM space using prompt-based supervision on next-item properties (brand/category/title/description), and (2) RERI (Retrieval-based Recommendation via Image features), which retrieves items from the corpus with user representations derived from an LLM token [REC], projected to a shared space and compared via dot products. Experiments across four Amazon categories (Sports, Grocery, Art, and Phone) demonstrate that image-based representation significantly outperforms attribute- and description-based LLM baselines, achieving about 2.93× faster inference while delivering roughly 22% performance gains over attribute-based representation. Additionally, this approach shows enhanced robustness under tight context budgets and is more resilient to noisy descriptions.", "questions": "1.  Your SOTA results in Table 1 (e.g., 0.4570 Hit@5 on Sport) appear to match row (e) of your ablation study in Table 2, which is the **\"Image + CF + Text**\" variant. How do you justify the title \"Image is All You Need\" when your best-performing model explicitly requires **text features** in its retrieval module? Given that your actual contribution seems to be moving text features from the (inefficient) LLM prompt to the (efficient) retrieval head, would a more accurate title be \"Images as an Efficient Prompt Representation for Multimodal Recommender Systems\"?\n\n2. In Section 4.2, you claim text-specific information has \"little impact,\" using the `I-LLMRec+D` experiment as evidence. However, your own ablation study (Table 2, row (d) vs (e)) shows that adding \"Text\" features to the retrieval module *improves* performance (e.g., Sport Hit@5 from 0.4491 to 0.4570) . How do you reconcile this direct contradiction?\n\n3.  Does the `I-LLMRec+D` experiment (text in prompt) not simply prove that text in the *prompt* is redundant *if* text is already being used in the *retrieval head*, rather than proving that text information *itself* is useless?\n\n4.  Do the inference times reported in Figure 3 include the wall-clock time for **visual feature extraction** (i.e., the SigLIP encoder pass)?\n\n5.  If the visual features were pre-computed, what is the actual latency of this feature extraction step per item, and how does this cost compare to the LLM processing time (which you optimized)? In a real-time system, wouldn't this encoder cost be a significant bottleneck?\n\n6.  You justify your entire premise on a CLIP cosine similarity of ~0.31, calling it \"significant overlap\". Given that a 0.31 correlation is generally considered weak, why do you believe this is strong enough evidence to claim that images can semantically *replace* detailed text descriptions?\n\n7.  All your experiments were conducted on retail and fashion datasets (Sports, Art, Clothing, etc.) where items are inherently visual. How do you expect I-LLMRec to perform in **visually-weak domains**, such as recommending books (where the cover art has low semantic value), news articles, or financial services?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **Significant and Practical Problem:** The paper addresses a critical and practical problem in LLM-based recommender systems: the trade-off between efficiency (low token usage) and effectiveness (rich semantic representation). It clearly diagnoses why attribute-based methods are efficient but semantically poor, while description-based methods are rich but computationally expensive.\n\n2. **Excellent Efficiency and Scalability:** The core engineering contribution is demonstrating that using images as a prompt representation is highly efficient. Figure 3 provides strong evidence that the inference time of `I-LLMRec (I)` (red line) remains low and stable as the user sequence length (`|S_u|`) increases, whereas text-based methods like `TRSR (D)` (black line) and `I-LLMRec+D` (gray line) scale poorly due to their reliance on lengthy descriptions.\n\n3. **Robustness to Context Window Limits:** The analysis in Figure 4 is a key strength. It shows that `I-LLMRec`'s performance is stable even with a severely constrained context window (e.g., 256 tokens). In contrast, description-based models suffer a sharp performance decline as the context window shrinks, highlighting `I-LLMRec`'s practical value in token-limited environments.\n\n4. **Novel Alignment Methodology:** The proposed Recommendation-oriented Image–LLM Semantic Alignment (RISA) module is a clever contribution. Instead of relying on generic image–caption alignment, it trains the adaptor (M) to align visual features with recommendation-specific properties (e.g., brand, category, title, description). The ablation study (Table 2, row (a) vs. (b)) confirms that this task-specific alignment is crucial, leading to significant performance gains.\n\n5. **Clear Analysis of Text Robustness:** Figure 5 provides a clear empirical argument for the robustness of images over text. It demonstrates that text-based approaches are vulnerable to either information loss from summarization (Fig. 5a) or performance degradation from noise in full descriptions (Fig. 5b).", "weaknesses": "1. **Title and Premise are Overclaimed:** The paper's title, \"Image is All You Need\", and its central premise are fundamentally contradicted by its own best-performing model. The SOTA results reported in Table 1 for \"I-LLMRec (I)\" are, according to the ablation study (Table 2, row (e)), achieved by a model that fuses Image + CF + Text features. This model explicitly requires textual features ($r_{u,i}^{lext}$) in its RERI retrieval module to achieve top performance. The paper does not replace text; it moves text from the (inefficient) LLM prompt to the (efficient) retrieval head. This is a major overclaim.\n2. **Incomplete Efficiency Costing:** The efficiency gains reported in Figure 3 are potentially misleading. The paper does not clarify whether the inference time includes the (potentially significant) computational cost of extracting visual features using the SigLIP encoder. If these features are assumed to be pre-computed, the reported wall-clock times do not reflect the full cost of a real-time recommendation.\n3. **Weak Justification:** The entire motivation rests on the \"significant information overlap\" between images and text 。The primary evidence is a CLIP cosine similarity of ~0.31 (Figure 1c) 。This value is not self-evidently \"significant\" or \"surprisingly high.\" It is only marginally higher than COCO positive pairs (0.26)  and is very far from a strong correlation. This justification is weak and feels like an overstatement to fit the narrative.\n4. **Flawed Argument on Textual Information:** The paper claims in Section 4.2 that text-specific information \"has surprisingly little impact on recommendations\" 。The evidence cited is that I-LLMRec+D (adding text descriptions to the prompt) did not improve performance。This is a flawed argument. This experiment only proves that adding text to the prompt is redundant (which Figure 3 confirms is also inefficient)，not that text information itself is useless. In fact, the paper's own ablation (Table 2, row (d) vs. (e)) directly disproves this claim, showing that adding \"Text\" features to \"Image + CF\" features improves performance (e.g., Sport Hit@5 from 0.4491 to 0.4570; Art Hit@5 from 0.5795 to 0.5883) 。This confirms that text is impactful and necessary for the model's best performance.\n5. **Limited External Validity:** All datasets used are from the retail and fashion domains. These are visually-driven domains where this method is predisposed to succeed. The paper provides no evidence of generalization to visually-weak domains (e.g., books, news, services).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RCPmiJP5ol", "reviewer": "Reviewer_1MNz", "summary": "The paper proposes an LLM-based recommender framework that replaces lengthy product descriptions with product images as the primary carrier of semantic information to reduce token usage and inference overhead while preserving recommendation quality. The method aligns visual features to the language model’s embedding space through a lightweight adapter trained with a recommendation-oriented prompting scheme, and then replaces text generation with a retrieval-style ranking head that scores real candidate items.", "questions": "see weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. **Insightful reframing of the input modality under tight token budgets.** The paper clearly identifies the practical bottleneck of long textual inputs for LLM recommenders and argues—backed by empirical analyses of image–text redundancy—that images can serve as compact proxies for much of the descriptive semantics; this reframing is valuable because it opens a path to maintaining recommendation quality while substantially reducing context length, especially in settings where text is verbose, noisy, or unevenly curated.\n    \n2. **Well-scoped, efficiency-oriented architecture that avoids over-generation.** By aligning image features to the LLM and using a retrieval head rather than free-form generation, the approach ensures that outputs correspond to real catalog items, simplifies deployment, and naturally supports score-level fusion with collaborative-filtering and optional textual features; this design choice balances effectiveness with operational efficiency and makes the method easier to integrate into existing candidate-generation and re-ranking stacks.\n    \n3. **Comprehensive within-setting analyses that illuminate robustness and design trade-offs.** The experiments, while on public datasets, go beyond headline metrics to examine sensitivity to context window size, the impact of missing or degraded images, the benefits in cold-start regimes, and ablations that separate the roles of the alignment adapter and retrieval module; these studies help practitioners understand where the method is most reliable and how to combine it with other modalities without assuming that discarding text is universally optimal.", "weaknesses": "1. **Limited evidence for scalability to industrial-scale deployments.** Experiments rely on small, public datasets and do not convincingly demonstrate behavior under production constraints such as large candidate pools, high user/item cardinalities, tight latency/service-level targets, and strict token budgets; to strengthen external validity, the paper should report system-level metrics (e.g., throughput, memory footprint, time-to-rank, cost per request) alongside $Hit@K$/$NDCG@K$ under increasing catalog sizes and candidate set growth, include stress tests with realistic traffic patterns and cold-start skew, and, if proprietary data are unavailable, simulate scale (e.g., catalog expansion, multi-tenant load) to approximate industrial conditions.\n    \n2. **Relying on images while discarding available text is counterintuitive and under-justified.** Although the paper argues that product images capture much of the semantic content of descriptions, in practice text often provides complementary signals (attributes, specs, usage constraints, safety notes) and is essential in low-visual domains; a more convincing case would compare early/late fusion and gating strategies, include confidence-aware fallbacks when image quality is poor or missing, analyze error cases where text helps, and broaden coverage to text-centric categories (e.g., books, apps) to verify whether image-only inputs remain competitive when visual cues are intrinsically weak.\n    \n3. **Comparisons lack strong compressed-text or lightweight language baselines.** It is premature to conclude that long descriptions should be replaced rather than compressed without benchmarking against competitive text pathways that control for compute and token budget; recommended baselines include (i) learned summarization to fixed length with budget-aware prompts, (ii) small/distilled LMs or sentence encoders with adapter/prefix tuning, (iii) classical text features (TF–IDF/BM25) with a learned re-ranker, and (iv) category/attribute tokenization with compact encoders; for fairness, the paper should equalize total trainable parameters, training wall-clock, and inference budget across modalities and report $NDCG@K$/$Hit@K$ under the same token caps to verify whether images truly dominate when text is efficiently compressed.\n    \n4. **Methodological fairness is unclear given image-side tuning and a frozen LLM.** The current setup tunes an image adapter while leaving the LLM unfine-tuned, which may disadvantage purely textual baselines and conflate modality choice with where adaptation capacity is allocated; to ensure apples-to-apples comparisons, the authors should either (a) freeze both pathways symmetrically or (b) allow comparable adaptation on the text side (e.g., LoRA, prefix-tuning, lightweight adapters) under a matched parameter/cost budget, and further ablate the contribution of the visual adapter versus the retrieval head to isolate where gains originate, ideally reporting robustness under domain shift and varying rates of missing or low-quality images.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qsIdAgLRw6", "reviewer": "Reviewer_11jm", "summary": "The paper studies how to represent items for LLM-based sequential recommendation and argues there’s an inherent efficiency–effectiveness trade-off between attribute-only text and description-rich text. It proposes I-LLMRec, which replaces long descriptions with images, passing a single visual token per item into a frozen LLM via a learned adaptor (RISA) to align visual features to the language space, and a retrieval objective (RERI) that directly retrieve items from the item pool by leveraging the item images.", "questions": "- What is the average total token count per item instance in practice (title + delimiters + [VISUAL]) across datasets?\n- Could you explore how TRSR/TALLRec perform title-generation with beam search (their native setting) and compare to your retrieval at equal candidate sizes?\n- How about the scalability to large e-commerce datasets? The authors should discuss the complexity of this method.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The efficiency-effectiveness problem is well-motivated, with supporting analysis and the observation of image-description semantic overlap.\n- Extensive experiments on four Amazon dataset demonstrating the effectiveness of the proposed method, with ablation study proving the contribution of each module.\n- The method is less sensitive to noisy descriptions and handles 50% missing images by backing off to text features, still outperforming text-only baselines.", "weaknesses": "- The real-world deployment trade-offs need to be reconsidered, and how about the latency of visual encoder cost, adaptor and LLMs.\n- The paper analyzes item coldness, but an explicit study of short user histories (cold-start users) is unexplored.\n- How about the performance of baselines if we add image information?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)