Preference Discerning with LLM-Enhanced Generative Retrieval
Authors: Fabian Paischer
Liu Yang
Linfeng Liu
Shuai Shao
Kaveh Hassani
Jiacheng Li
Ricky T. Q. Chen
Zhang Gabriel Li
Xiaoli Gao
Wei Shao
Xue Feng
Nima Noorshams
Sem Park
Bo Long
Hamid Eghbalzadeh
Venue: Accepted by TMLR ()

Abstract: In sequential recommendation, models recommend items based on user's interaction history. To this end, current models usually incorporate information such as item descriptions and user intent or preferences. User preferences are usually not explicitly given in open-source datasets, and thus need to be approximated, for example via large language models (LLMs). Current approaches leverage approximated user preferences only during training and rely solely on the past interaction history for recommendations, limiting their ability to dynamically adapt to changing preferences, potentially reinforcing echo chambers. To address this issue, we propose a new paradigm, namely *preference discerning*, which explicitly conditions a generative recommendation model on user preferences in natural language within its context. To evaluate *preference discerning*, we introduce a novel benchmark that provides a holistic evaluation across various scenarios, including preference steering and sentiment following.  Upon evaluating current state-of-the-art methods on our benchmark, we discover that their ability to dynamically adapt to evolving user preferences is limited. To address this, we propose a new method named Mender (**M**ultimodal Prefer**en**ce **D**iscern**er**), which achieves state-of-the-art performance in our benchmark. Our results show that Mender effectively adapts its recommendation guided by human preferences, even if not observed during training, paving the way toward more flexible recommendation models.

Published in Transactions on Machine Learning Research (07/2025)
Preference Discerning with LLM- Generative Re- trieval
Fabian Paischer
ELLIS Unit, LIT AT Lab, Institute for Machine Learning, JKU Linz, Austria AI at Meta
Liu Yang
University of Wisdiso AI at Meta
Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadehi beghbala@meeta AT at Meta
Reviewed on OpenReview: htips: // openreview. net/ forum? id- Tdmrod
Abstract
In sequential recommendation, models recommend items based user's interaction history. To this end, current models usually incorporate information such item descriptions and user intent or preferences. User preferences are usually not explicitly given in open-source datasets, and thus need to be approximated, for example via large language models (LLMs). Current approaches leverage approximated user preferences only during training and rely solely on the past interaction history for recommendations, limiting their ability dynam- ically adapt to changing preferences, potentially reinforcing echo chambers. To address this issue, propose new paradigm, namely preference discerning, which explicitly condi- tions a generative recommendation model on user preferences in natural language within its context. To evaluate preference discerning, we introduce novel benchmark that provides a holistic evaluation across various scenarios, including preference steering and sentiment following. Upon evaluating current statc-of-tho-art methods on our benchmark, we discover that their ability to dynamically adapt to evolving user preferences limited. To address this, we propose a new method named Mender (Multimodal Preference Discerner), which achieves statc-of-the-art performance in benchmark. Our results show that Mender effectively adapts its recommendation guided by human preferences, even if not observed during training, paving the way toward more flexible recommendation models.
1 Introduction
Traditional sequential recommendation refers to the task recommending items to users based on their historical interactions. This requires inferring latent. variables, such as user preferences and intents, which are often not explicitly provided in common datasets, as information about users is usually scarce. Therefore, current works use LLMs to approximate user preferences from the users' interaction history {Zheng et all,
2023
Cao et al. 2024 Oh et al. 2024 or user reviews about items (Kim et al. 2024). These preferences are then used as targets for auxiliary tasks CCoo et al. 2024 Zheng et al. 2023 instructions for retrieval (Oh)
et al. 2024). or step-by-step reasoning (Kim et al.
2024
Incorporating such information usually improves
recommendation performance.
"Code available htttss//ttthhhccce/eeeeeeesssees
21: Corresponding author.

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Published in Transactions on Machine Learning Research (07/2025)
Preference approximation
Preference conditioning
User data
Prompt
User preferences
Recommscnaaioo
Review
Preference
Multimodal sequential recommendation system
Review
Preference
LLM
Interaction History
Review
Preferonce
Review
Preference
Figure 1: The preference discerning paradigm consists of two phases: preference approzimation and preference conditioning. In preference approxination phase, pre-trained LLM is used to infer user preferences from user-specific data. In preference conditioning phase, a sequential recommendation model is conditioned on the generated user preferences, enabling personalized recommendations.
Current sequential recommendation models lack the ability to dynamically adapt to changing user preferences after training, as they rely solely on the past interaction history of a user. Consider a scenario in which a user interacts on a social media platform and primarily views certain contents. Current recommendation models will continue to recommend similar content as no other information is provided to them. However, the user's interest may change over time influenced by lifestyle changes, career transitions, hobbies, life events. For example, user who uses social media for entertainment might start learning new skill, but still receives viral videos instead of tutorials. This issue may be mitigated by users providing their preferences to the recommendation model; however, this ability is lacking in available models. To adapt to such situations, they require re-training after the user interacts with different items. Furthermore, there is a lack of understanding to what extent current rocommendation models accurately discorn user preferences.
To address these limitations, we propose a novel paradigm, which we term preference discerning. The aim of preference discerning is to approximate user preferences from previous comments, reviews, or recent activity and to provide them to the rocommendation modcl, such that it can dynamically adapt its recommendations. Preference discerning consists of two slages: (1) preference approxination and (2) preference conditioning. In the first stage, we use LLMs to distill user- and item-specific data into short and concise user preferences. The second stage trains a sequential recommendation model conditioned on the generated preferences in its context to generate item recommendations. This in-context conditioning unlocks steering via generated user preferences, effectively combining the sequential prior from interaction history with the user preferences. This allows users to specify the item properties they wish to avoid or prefer in natural language. The model then integrates this information with previous interactions to dynamically adapt the recommendation.
To evaluate preference discerning capabilities of sequential recommendation models, we propose a holistic benchmark that comprises five evaluation axes: (1) preference-based recommendation, (2) sentiment fol- lowing, (3) fine-grained steering, (4) gaarse-rrained steering, and (5) history consolidation. We evaluate state-of-the-as generative retrieval methods on our benchmark and find that they lack several key abilities of preference discerning. Therefore, we introduce a novel multimodal generative retricval method named Mixedmodal preference discerner (Mender) that effectively fuses pre-trained language encoders with the generative retrieval framework (Rajput et al. 2023 for preference discerning. We demonstrate that prefer- ence discerning capabilities can naturally emerge when training solely on preference- based recommendation data. Furthermore, we show that preference discerning capabilities can be obtained by augmenting the train- ing data with training splits for the different axes. As a result, Mender can be effectively steered by different user preferences provided in its context to recommend specific items. Ultimately, Mender outperforms the existing state-of-the-ee the-rrrttttrrttooeeoeeeee- generative retricval models on most evaluation axes of our benchmark. In summary, our contributions are as follows.
We introduce novel paradigm called preference discerning, where the generative recommendation model is conditioned on user preferences within its context.
We propose comprehensive benchmark for evaluating preference discorning, comprising of five distinct evaluation scenarios that provide holistic assessment of its capabilities.
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We present Mender, multimodal baseline that integrates collaborative semantics with language preferences, achieving state-of-the-art performance on our proposed benchmark.
2 Related Work
Sequential Recommendation can be categorized into two major scenarios: search (Nigam et al. 2019) and recommendation (Covington et al. 2016). The former assumes access to a query from user that reflects their intent. (He et. al.
2022), whereas the latter scenario does not make such an assumption. For the
recommendation scenario, numerous works have investigated the use of additional information to enhance recommendation performance Meng et al.) 2020 Hidasi et al.) 2016 Liu et al. 2021 Zhang et al. 2019aa Bogina & Kuffik 2017 Li al. 2020p. Our work introduces a new paradigm that enables in-context steering of sequential recommendation models through textual user preferences.
Existing Benchmarks for recommendation vary in their representation of user preferences and the tasks they evaluate. Oh et al. (2024) proposed benchmark for instruction-followii in information retrieval where instructions are generated from search queries, The C4 benchmark (Hou et al., 2024) uses complex scarch quories that reflect user preferences for retricval. Contrary, we focus on user preferences in sequential recommendation. Such preferences are often modeled indirectly from user queries and responses to recom- mended items (Min et al. 2023; Huang et al.] 2013 Ma et al.) 2018), or represented as edges on graphs (Ying) et al. 2018 Li et al.) 2019). In query-aware scquential recommendation [Hc et al. (2022) the model is given keywords in its context that represent the user's intent but do not capture their preferences. In contrast, our benchmark builds on established datasets (Ni et al., 2019 Kang & McAuley
2018 and augments them
with generated user preferences to evaluate preference discerning capabilities.
Generative Retrieval uses autoregressive modeling to generate the next item, rather than performing pairwise comparisons between a user representation and all item representations. The promise generative retrieval is efficient operation on industrial-scale item sets (Singh et al., 2024). Therefore, in our work we focus mainly on applying preference discerning to generative retrieval. Rajjut et al. (2023) proposes tokenizing items in the form of semantic IDs <Lee et al. 2022) instead of random IDs. The benefit of this approach is that very large item sets can be represented as a combination of ids that reflect their semantic similarity. Subsequent works have investigated the effect of learned tokenization (Sun el al. (023) and additional objectives (Li et al. 2024 Wang et al.) 2024).. Our Mender represents items as semantic IDs and fuses them with pre-trained LMs to effectively steer the rocommendation.
Language-Based Sequential Recommendation rely on the premise of enhanced transparency and ac- tionable interrogation of recommendation systems (Radlinski al. 2022). Furthermore, language provides natural interface for users to express their preferences. Recent works have used LLMs to approximate user preferences by representing user and item specific data in natural language (Zheng et al., 2023 Oh et al. Cao et al.) 2024), by conditioning the LLM on user embeddings (Ning et al. 2024 or by leveraging user reviews of items (Kim et al. 2024 In this context, Kang et al. (2023) found that an effective preference approximation may require fine-tuning of the LLM. Other studies have explored the use of LLMs for data augmentation in sequential recommendation GGeg et al.) 2022; Zhang et al., 2019b] Luo al. 2024). In
2024
the near-cold start scenario, [Sanner al. (2023 demonstrated that user preferences represented in natural
language can be partieularly effective. Li et al. (2023) showed the benefit of moving from ID-based repre- sentations to text-based representations of the interaction history. Similarly, Petrov & Macdonald (0023)
represents all items in natural language and performs a ranking conditioned on past interactions.
Zheng
ct al. 2023) explored aligning semantic IDs with natural language by adding auxiliary tasks.
3 Methodology
We first elaborate on the lask of sequential ial recommendation and give important background in Section 3.1 In addition, we elaborate on the core components of preference discerning, namely preference approzimation and preference conditioning (see Fig. []. We cover our preference approximation pipcline in Scction 3.2 and claborate on our proposed preference-conditioned method Mender in Section 3.3 and Section 3.4 Finally, we elaborate the construction of our benchmark to evaluate preference discerning capabilities in Sention
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3.1 Background
In sequential recommendation, the goal is to provide personalized recommendation for users based on their interaction history. To this end, we assume access to set of users U and set of itemns For each user u E U, we assume access to a sequence of item interactions in chronological order: = [ . (T=)) where Tu represents the time horizon of a particular user u who has interacted with items in E Z. The task Traditional sequential
of sequential recommendation is then to predict item .(2i)) given (.(1)
..... ((F.-11)
recommendation systems (Kang & McAuley 2018 Zhou et al., 2020 Sun et al. 2019 Hidasi et al. 2016)
are based on sequence modeling architectures (Hochreiter & Schmidhuber 1997 Devlin et al. 2019 Vaswani
et al. 2017) to represent users and items via dense embeddings. These embeddings are then leveraged to retrieve the most relevant items for a user via pariwise comparisons using maximum inner product search. This approach can be computationally expensive as the number of items grows and each item must be represented as an embedding to be stored. Generative retrieval (Rajput et. al., 2023) aims at alleviating the need for pairwise comparisons and storing unique embeddings for each item by leveraging semantic IDs [Lee ct al. 2022) in combination with generative modeling. These approaches have proven to be effective on vndustrial-scale item sets (Singh et al. 2024
3.2 Preference Approximation
Preference approximation refers the process of distilling user- and item-specific data into short and concise user preferences. This compression is crucial, as sequential recommendation models are usually limited by the amount of information that be provided in their input. This information may include user reviews, profiles, posts, demographic information, or any other relevant details. Furthermore, incorporating item- specific information is crucial, as it provides additional context that can help alleviate the vagueness or incompleteness often encountered in user-specific data. Preference approximation is necessary prerequisite that enables in-context conditioning on the generated user preferences.
To approximate user preferences, we as- sume access to user-specific data includ- ing user reviews Fa R and descriptions of items in natural language. For each user u and for each timestep 1 < t S Tu. collect reviews {rll), ......a 10) along with item information (e.....,46 from their interaction history 8n and prompt an LLM to approximate the user's pref- erences. We add a prompt (see Ap- pendix C to the interaction history that contains general instructions such as ignoring aspects such delivery time or pricing, and encode aversions of the user. With this process, we oblain a set of five user preferences (( for each timestep t based on past interactions. Importantly, the information contained in the different user preferences P!!) is mostly orthogonal, i.e., each preference refers to different items or item properties (see an example in Appendix Ch. To verify the quality of the generated preferences, we conduct a manual confimmation study (sce Appendix F). The participants found that around 75% of the generated preferences correctly approximate the user's preferences. A schematic illustration of the preference gener- ation procedure is shown in Fig. [0 along with pseudocode in Algorithm [ For details prompts, the generation process, or the granularity of preferences, we refer to Appendix
A Algorithm 1 Preference Approximation
Input: prompt æ, users U, items I, reviews R, Language Model LLM(-),user sequence length To
1: for E U do
2: for E {I,..., Tr do
3: p!0 + LLM
x; tu (1)
4: end for
5: end for
3.3 Multimodal Preference Discerner (Mender)
Dynamically adapting to evolving user preferences requires the recommendation model itself to be condi- tioned on them, which leads recommendation stecrability. Steerability in this context can be defined as guiding a recommendation model towards or away from certain items, based on the provided preferences and their given context. For example, if user preferences are given as uuser prefers AR devices prioritizing
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comfort and minimal eye strain" (see Figure 10), the recommendation model must steer its recommendations towards items relevant in this context. To achieve steerability, we perform preference conditioning.
In preference conditioning, we explicitly provide generated user preferences as an additional input to the sequential recommendation model. This requires the model to be capable of processing natural language imput and to predict item identifiers. Therefore, we design recommendation model that efficiently fuses the generated user preferences with item descriptions in its context and predicts item identifiers. This results in our new method, Mender, novel multimodal generative sequential recommendation model. Mender builds the TIGER [Rajput et al. 2023 generative retrieval model trained using semantic IDs. These semantic IDs are obtained by training RQ-VAE (Lee et al. 2022) on embeddings of items in the Sentenco- T5 space. Given an item embedding e E Rd, the RQ-VAE quantizes e into a discrete feature map as:
N, = -(k- (KI,...,KN) E [K]]
(1)
where C represents a finite set of tuples (Ik,Cc))kEK K denotes the gramularity of the codcbook C with embeddings [ca|1 < k S K] and N corresponds to the depth of the RQ-VAE, i.e,, the number of codebooks. A user sequence s, is then represented as sequence of semantic IDs: [x(]]. ..... x ....., ,....,,((T.) (TT)) (TT)))
which serves input to train Transformer model (Vaswani et al. 2017 To enable conditioning natural language, we leverage pre-trained language encoders. Specifically, we represent both the interaction history and the user preference in natural language and process them with a pre-trained FLAN--7-Small encoder (Chung et al.) 2024). This is inspired by Li et al. (2023 Paischer et al. (2022 2023 who demonstrated the benefits of history compression using natural language. The decoder of Mender is randomly initialized and conditioned on the language encoder through cross-attention to predict semantic IDs. Since semantic IDs are represented in separate embedding space than natural langauge, Mender can be classified as multimodal.
3.4 Mender Variants
To strike a balance between efficiency and performance, we fur- ther propose two variants of Mender, namely Menderrak and MenderEmb- The key difference between these variants lies in the way they encode user preferences and items. Menderrok encodes user preferences and items as single sequence of lan- guage tokens. Hence, this model provides a performant and high-capacity model with strong language understanding ca- pabilities. However, MenderTok processes the entire token se- quence at once, making it suitable for complex language-basen in-context learning and fine-tuning. In contrast, Mender Emb encodes each user preference and item separately using pre- trained embedding model from SSu et al. (2023). Menderemb allows pre-computing item and preference embeddings, result- ing improved training elficacy. Mendereab does not require fine-tuning, as propagating through the embedding model for each preference/item is prohibitively expensive.
Attennttteiion
Decoder
LM Encoder
User preference
305
5
Interaction history
Figure 2: Mender. The decoder generates semantic IDs conditioned on user prefer- ences and interactions via cross-attention with pre-trained language encoder.
3.5 Evaluating Steerability via User Preferences
Stocrability can come in various forms, and there is no conventional way to evaluate it. In this work, we define five performance axes that shall evaluate different aspects of steerability (see Fig, 3
Preference- based Recommendation: recommending the correct item based on user preference and interaction history
The ability to steer lowards items that are similar (fine- grained) or very distinct (coarse-grained) to items observed during training
Fine & Coarse-Grained Steering:
Sentiment Following Capabilities: the ability to understand the sentiment in steering (e.g, user
likes something vs. user does NOT like something)
History Consolidation: the ability of the model to incorporate multiple user preferences.
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Fine-Grained & Coarse-Grained Steering. To evaluate the ability of the model to discern pref- erences that are semantically related to items, we introduce the tasks of fine- and coarse-grained steering, Recall that the preference-basee based recommendation scenario captures the underlying uncertainty of the origi- nal recommendation task we provide the model with pu - to predict <((). This result in cases where Pu (t-1) is not semantically related to :0) since often ((9) not related to previously acquired items. Therefore, our goal quantify the model's ability to be stcered towards items that are either semantically similar Published in Transactions on Machine Learning Research (07 (075))
Preference-based Recommendation
Sentiment Following
AR devices
Avoid bulky headsets. Find bulky headsets.
prioniizing comtort.
?
Fine-grained steering
Newest AR devices.
?
History Consolidation
Avoid bulky headsets
Coarse-grained steering
Curved OLED
devices priorilznng comfor,
?
?
On-eat headsets..
Figure 3: Five evaluation axes for preference discerning we focus on in this work: Preference based Recom- mendation, Fino-grainod stocring, Coarso-grained stocring, Sentiment following, and History Consolidation. Preferences highlighted in green indicates that they are unseen during training.
We provide a more in-depth definition of these performance axes the following paragraphs.
Preferenco-based Recommendation. This ovaluation sccnario extends the sequential recommendation scenario by incorporating the generated user preferences. For this lask, the model receives single user preference the set p(t-1) along with the interaction history and must predict the next item :((). We select the preference that yields the maximum cosine similarity to z(0) in pre-trained sentence embedding space
(t)
Ni et al. 2022). More formally, given a pre-trained sentence embedding model Φ(-), we select pa as
ph arg max p(p) (it) pEP(t-1)
(2)
[
This results in a setting where each ground truth item ( is associated with a single user preference pa
Therefore, the imput to the sequential recommendation system is sequence of [p (1) ,...,,? (...,,((-1) ((t-1); and the task is to predict <(+)_ Since pu is generated based only on information about past items (p E p((-')), there is no information leak that could reveal the ground truth item, that is, there no information leak and the underlying alcatoric uncertainty of the task is preserved.
or very distinct from (t) by modifying the user preference in its context. To achieve this, we identify a very
2(1) similar item ((t) and very distinct item to the ground-truth item iu by
70) arg max o(1) T (T()))
(ET)((")) (1)(1) (( "") and 200) aig o(1) T
(( (9) ( |l ((o((())
(3)
Next, associate 300 and 20 with different user preferences and P2 by
d(p) T ((())))
<(p) T 6(!
Pi arg max
[p(p)) (((o))))'
and P2 arg max PEP
(4)
PEP

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where P denotes the set of accumulated preferences across all users and items. Additionally, we obtain a target user à with the same ground truth item ( = but a different interaction history. The motivation for this is to enhance the variability in the generated datasets. By combining these elements, we create two 1/
new sequences: P1: P..'......... ......g (t-1)]
(1)
and P2, p2.24 "
((-1)) with ground-truth items 7 and ?(t)) respectively.
A visual illustration of this procedure is provided in Fig. 13 Throughout the dataset creation process, we ensure that the preferences used during training are not associated with the evaluation items. This allows to evaluate the model's s ability to generalize to new preferences not observed during training.
Sentiment Following, An important aspect of preference discerning is whether the recommendation model comprehends the sentiment of user preferences. A user may provide negative preferences about items or properties that should be avoided To assess the ability of recommendation models to identily and follow the sentiment of issued preferences, we attempt to establish a causal relationship between items that received a negative review and negative preferences generated during the preference approximation stage. The intuition is that a negative user preference generated by the LLM is likely caused by an item in Sy that received a negative review. Therefore, we first. need to identify negative reviews and user preferences. This is done using pre-trained sentiment classifier (Hartmann et al.) 2023). Then we match each negative user preference Pu at timestep t with a negative review in frl"" j و The matching is again done via cosine similarity in a pro-trained Sentence-TY space (Ni ct al., 2022) This results in tuples (PT .), where Pa represents a negative preference and denotes the item that received the negative review to which the preference was matched. To obtain positive pair (pt,)), 1), we apply rule-based inversion heuristic to the negative preference (see Appendix D for details). The compiled data consist solely of (pā.i) tuples without interaction history. During evaluation we provide the preference in the context of the model and it should either predict or not predict the item based on whether the provided preference is positive or negative.
t}.
To evaluate sentiment following, we rely on a combined hit-rate measure. Given a set of k predicted candidate items C = ..i.... J.), we check whether the ground truth item occurs in C (that is, Lc(i) 1, where 1 1() represents the indicator function). In practice, we obtain two sets of predictions C+ and C=, where C+ is obtained using positive preference pt and C- using the negative preference Pe for item i. The combined hit rate measure is computed as m lle (i) A -1c- (i) where m = indicates that the model successfully recovered the item for pt. while simultaneously not predicted it for P.. This measure can be calculated for different sizes (k) of prediction sets as m@kk
History Consolidation. A user may have multiple preferences about different items. In such a case, the sequential recommendation system must incorporate multiple user preferences to steer their recommendation. Therefore, our aim is to evaluate the ability of the system to incorporate multiple user preferences, some of which are potentially not related to the next item. To simulate this, simultancously provide the model with the five generated preferences P!(-") to predict the ground-truth item o!() This evaluation scenario can be considered more difficult than preference- based recommendation, as it potentially has a higher noise ratio in the provided preferences. In this evaluation scenario, the preference originally matched is contained in the set of accumulated user preferences P. Therefore, in order to accurately predict the ground truth item, the model must infer the matched preference from P. The corresponding evaluation sequences are structured as (T.-1) (1) puj ,P,., i1, 2i,,..,, (f...,,(..-)))) and contain all five generated user preferences.
4 Experiments
We evaluate our approach on four widely used datasets, namely three Amazon reviews subsets (Ni el al. 2019) and Steam (Kang & McAuley 2018 An overview of the dataset statistics can be found in Table in Appendix B To generate user preferences, we utilize the 1aaMa-37705 classification, we employ the model trained by Hartmann et al. 2023 The resulting preference statistics, including the number of generated preferences, the proportion of positive and negative preferences, and the sample sizes for each evaluation split, are presented in Table Our data generation pipeline is built entirely on open-source models, making it easily extensible additional datasets.
moddll. - model. For the sentiment
3 hhttps//huuggingffee.o/// Ilaaa/ Meta-Llama-33 Llama-3-700000 7-0-a----7----strre eenteeeeettsttteettteeeeee noberta--lrge--ngllla
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TIGER
Menders
VocabExtegp
Menderye
VocabExt.
Recommundation
Recommendation
Recommendaton
0.844
0,043
grained
Conree grainad
Froer grained
Coavee-r grainad
grained
Cone grained
0.055
0010
0.00z 0n
bosss
0.904
Hutery Comniidation
0722
Mistory Comnoiiation
Nottry Comolidation
0.013
Sontimemt
Sandeannd
(a) Beauty
(b) Sports and Outdoors
(c) Toys and Cames
Figure 4: Recall@10 for all methods on our novel benchmark, evaluating preference discorning across three subsets of the Amazon review dalaset: Beauty (4a] Sports and Outdoors (4b), and Toys and Games
(4cp. Menderrok mostly outperforms generative retrieval competitors across Recommendation, F'ine-gruined steering and History consolidation. All methods struggle Sentiment following and Coarse-groined steering.
For training our models, we use the preference-basee recommendation data, which consists of single user preference and the interaction history. Unless mentioned otherwise, the additional generated cata splits (positive/negatiee and fine/coarse data) are used solely for evaluation purposes. Following (Rajput et al, 2023), we limit the maximum number of items in user sequence to the 20 most recent. ones. For the Beauty, Toys and Games, and Steam datasets, we found it beneficial to also fine-tune the language encoder, for which we use LoRA (Hu et al.) 2022). By default, we use the FLAN-T5-Small (Chung et al., 2024) language encoder for MenderTok- We adopt leave-last-out data split, where the penultimate item of a soquence used for validation and the last item used for testing (Kang & McAuley 2018 Sun et al., 2019).. Our evaluation benchmark is based only the validation and test items of that split along with their paired user preferences, that is, in each sequence are used for training, except for the first item, since no user preferences are available for it. We evaluate our trained baselines using common retrieval metrics, including Recall (or Hit Rate), and Normalized Discounted Cumulative Gain (Järvelin & Kekäläinen 2002 NDCG). Implementation details for training the RQ- VAE and Transformer models can be found Appendix A.1 and Appendix A.2 respectively. All our methods are trained on single A100 or V100 GPUs using PyTorch (Paszke et al. 2019
preferences for training and inference. The remaining items
4.1 Baselines
We train and evaluate range of generative retrieval baselines and compare their performance to Mender.
TIGER (Rajput et al. 2023 is a state-of-the-art generative retrieval model based on semantic IDs. Although TIGER is not conditioned on user preferences, we still evaluate its performance on our benchmarks for recommendation, fine-gra ane-grained steering, and coarse-grained steering. The latter two essentially evaluate how well TIGER predicts a very similar or distinct item to the ground-truth item.
VocabEXtRND is based on extending the vocabulary of the TIGER model, which allows it be conditioned language preferences. Notably, this version does not leverage any pre-trained components.
LC-Rec [Zheng et al. 2023) extends the vocabulary of pre-trained LM with newly initialized embeddings that represent semantic IDs. We fine-tune the LM using LoRA (Hu et al. 2022), but do not add the auxiliary lasks. Additionally, we reduce the dimensionality of the language model head to match the number of semantic IDs, as language generation is not required for our task.
VocabExtrM represents the past interaction history in language as done for MenderTok and MenderEmb- but initializes the decoder with a pre-trained language decoder. Therefore, this baseline operates on the same semantic gap the Mender variants. We again leverage LoRA for fine-tuning.
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4.2 Results
We present detailed analysis of the results obtained by the different methods on our benchmark for three subsets of Amazon reviews (Beauty, Sports and Outdoors, and Toys and Games) and Steam datasets. Fig. [ and Fig. 5a show Recall@10 for all methods on the Amazon and Steam datasets, respectively. Table [ also shows Recall@10 plus additional metrics, such as Recall@5, NDCG@5, NDCG@10, as well as relative im- provements of Mender over the best, baseline method. In Appendix []] we report the corresponding standard deviations for all methods on all datasots. Our results reveal several key trends: (i) incorporating prefer- ences consistently improves performance; (ii) training on preference-based recommendation data leads to the emergence of fine-graine grained steering on certain datasets; (iii) current models struggle with sentiment following; and (iv) both noarso-grained stecring and sentiment following can be achieved through data augmentations. Additionally, we provide ablation studies on data mixtures and the impact of adding user preferences.
Table 1: Performance for all methods on all evaluation axes for all datasets trained on recommendation data. We report average performance across three random seeds as well as relative improvements of Mender to the best performing baseline and highlight best performance in boldface. For sentiment following we report m@k for & E (5, 10} instead of Recall@k.
Sports and Outdoors
Beauty
Toys and Games
Steam
Methods
Recall 85
NDCC
Recall 010
NDCG
Recall 85
NDCC 80
Recall
NDCG
Rocail X35
NDCG
Recall 611
NDCG
Recull K35
NDCG 80
Rocall 010
NDCG
66
310
310
010
85
010
910
Recommendation
TIGER
0,01119
0.0158
0.0377
0.0199
0.0131
0.0275
0.0681
0.0356
0,0375
0.0238
0.0600
0.0311
0.1630
0,1440
0.1990
0.1330
Vocaa Exttann
0.0238
0.0151
0.0392
0.0201
0.0434
0.0277
0.0697
0.0362
0.0330
0.0205
0.0644
0.0275
0.1680
0.1420
0,2000
0.15-10
LC-Ree
0.0195
0.0124
0.0291
0.0155
0.0291
0.0731
0.0382
0.0327
0.0209
0.0256
0,1600
0.1370
0.1940
VocubEExtyy
0.0233
0.0148
0.0355
0.0187
0.0345
0.0224
0.0561
0.0293
0.0371
0.0234
0.0559
0.0296
0,1547
0.1305
0,1878
0.1412
Menderront
0.0261
0.0173
0.0391
0.0215
0.0191
0.0321
0.0755
0.0105
0.0122
0.0267
0.0653
0.0312
0.1455
0.1110
0.1820
0.1230
Menderna
0.0282
0.0188
0.0427
0.0234
0.0805
0.0401
0.0937
0.0508
0.0533
0.0346
0.0799
0.0432
0.1680
0.1440
0.2010
0.1560
Rel. Impe.
+13.2%
18.99%
+8.9%
+ +16.4%
+3..4%
436.4%
+28 15 + 33.0%
+ 42.1%
45.4%
+-3.2%
+38.9%
+1.2%
+0.0%
+2.0%
+1.3%%
Fine-grained steering
TICER
0.0061
0.0037
0.0118
0.0055
0.0119
0.0074
0.0195
0.0018
0.0149
0.0092
0.0237
0.0120
0.0084
0.0052
0.0145
0.0072
Vocai FoNgbF
0.0104
0.0063
0.0186
0.0080
0.0229
0.01663
0.0437
0.0220
0.0200
0.0123
0.0338
0.0174
0.0102
0.0064
0.0178
0.0088
LC-Ree
0.0074
0.0190
0.0097
0.0348
0.0218
0.0566
0.0288
0.0248
0.0153
0.0198
0.0157
0.0098
0.0264
0.0133
Voca Exx
0.0214
0.0132
0.0352
0.0176
0.0292
0.0186
0.0408
0.0253
0.0341
0.0220
0.0672
0.0294
0.0217
0.0133
0.0365
0.0180
Menderganh
0.0173
0.0108
0.0322
0.0154
0.0276
0.0174
0.0465
0.0234
0.0316
0.0199
0.0529
0.0267
0.0184
0.0114
0.0287
0.0147
Menderqa
0.0190
0.0117
0.0024
0.0150
0.0534
0.0344
0.0844
0.0444
0.0378
0.0237
0.0639
0.0321
0.0211
0.0134
0.0352
0.0179
Rel. Impee.
-1.66
-12.8%
-8.6%
-10.7%
+ +53.4%
+ 55.88%
+ 49.99%
+ 54.2%
+ 10.9%
+ +..7%
+ +11.7%
+9.2%
-2.8%
+ +1%
-3.7%
-1%
Coerse-grained steering
TIGER
0.0001
0.0000
0.00003
0.0001
0.0013
0.0001
0.0003
0.01102
0.00113
0.0001
0.0006
0.0000
0.0005
0.0003
0.00008
0.0014
Vocal VocabExttnN
0.0005
0.0003
0.0010
0.0001
0.0023
0.0014
0.0011
0.0021
0.0013
0.0009
0.0021
0.0011
0.0032
0.0018
0.0055
0.0026
LC-Hte
0.0006
0.0011
0.0009
0.0032
0.0019
0.0055
0.00228
0.0022
0.0013
0.0017
0.0022
0.0018
0.0114
0.0024
Vocal Eataaaataa
0.0047
0.0028
0.0098
0.00414
0.0053
0.0033
0.0086
0.0011
0.0037
0.0022
0.0065
0.0030
0.00117
0.0029
0.0077
0.0039
Menderrenn
0.0036
0.0012
0.0071
0.0033
0.0057
0.0035
0.0101
0.0050
0.0035
0.0021
0.0071
0.0032
0.0012
0.0021
0.00667
0.0032
Mendergak
0. 0.0023
0.0013
0.0045
0.0094
0.0059
0.0080
0.0032
0.0020
0.0060
0.0029
0.0043
0.0027
0.0081
0.00-10
Rel. Itape.
30.6%
-27.3%
-3%.1%
-33.3%
+77.4%
+ 78.8%
+-7775. + 81.8% -5.66%
-4.8%
+9.2%
+6.7%
-9.3%
-7.4%
+5.2%
+2.66%
Sentiment Sollowing
TICER
0.00000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Vocat TocabExtanN
0.0000)
(10000
0.0000
0.0000
0.0000
0.0000
0L0061
(LO0I86
LC-Reee
0.0018
0.0027
0.0029
0.00445
0.0008
0.0017
0.0033
0.0053
NocabExt2aa
0.0019
0.00166
0.0027
O. 1.0061
0.0012
0.0004
0.0049
0.0107
Mendergat
0.0022
0.0022
0.0030
0.0047
0.0017
0.0015
0.0114
0.0185
Mendorguk
0.00355
0.0042
0.0043
0.0053
0.0016
0.0017
0.0084
0.0110
Rel. Irape.
+ 84.2%
+555.6%
+-4.33%
+3.9%
+.4.7%%
+.0%
+86.9%
+72.9%
History consolidation
TIGER
0.00000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0030
0.0000
0.0000
Vocab VocabExdun
0.0190
(10120
(.0329
0.0161
0.0300
0.0191
010501
0.0256
0.0260
0.0158
0.01111
(10226
0.1366
0.1155
0.1612
0.1201
LC-Ree
0.0158
0.0101
0.0243
0.0129
0.0354
0.0226
0.0677
0.0297
0.0295
0.0185
0.0430
0.0229
0.1460
0.1277
0.1726
0.1363
Vocal Eattaaa
0.0179
0.0112
0.0278
0.01
0.0211
0.0155
0.0123
0.0211
0.0316
0.0195
0.0187
0.0251
0.0615
0.0140
0.0866
0.0521
Mendermant
0.0206
0.0133
0.0312
0.0167
0.0352
0.0228
0. 0.0680
0.0301
0.0314
0.0201
0.0616
0.021%
0.1241
0.0938
0. 1568
0.1040
Mender.aa
0.0151
0.0341
0.0187
0.0457
0.0304
0.0720
0.0388
0.0:667
0.0302
0.0700
0.0377
0.0
0.0317
0.0745
0.0399
Rel. Inpee.
+23.2% 425.88
+4.9%
+14.0%
4-34.5% +24.8% 30.-3000%4444 058766658..%655.00 454.9% +33.7% +50.2%
-15.1% -26.5%
-9.7% -23.7%
Recommendation. Our Mender achieves the best performance on all datasets on the recommendation
axis with relative improvements of up to 45%. The significant gap between TIGER and Menderrok demon-
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TIGER VocabExtewp
MenderEnt MenderTa
LC-Rec VocabExtLM
Menderna- Neg
Menderse- Coarsse
Mendersse
Menderra- Fine------ee
Mendern, Fine
Menderia Pos Neg
Menderna- All
Menderna Pos
Recomeccdaaiin
Recommendation
0.091
Fime- grained
Craase grained
grained
Coars grained
0,386
0.00
0.964
0,722
Hustory Convoidation
0113
History Contiidattio
Sontiment
Sontimont
(a)
Steam
(b) Beauty
Figure 5: Recall@10 of different baselines trained the default recommendation data of the Steam dataset (5a) MenderTok attains the highest performance Recommendation, but all methods struggle Steering and Sentiment following. 5b; Recall@10 for Menderrok trained on different datasplits on the Amazon Beauty subset. Menderna--Al leverages training data augmentation resulting in a universal model that performs well across all axes of preference discerning.
strates the benefits of conditioning the generated user preferences. Furthermore, provide comparison to traditional sequential recommendation baselines the recommendation task in Table Appendix [A.3 for the three Amazon subsets, which shows that our TIGER implementation outperforms those as well, and Mender further improves significantly on TIGER. In addition, MenderEmb performs second best Ama- zon datasets, providing a decent trade-off between performance and training speed, reducing training time around five fold. Notably, other baselines such as VocabExtRND and LC-Ree sometimes perform worse than TIGER on Toys and Steam, indicating that they cannot properly align the semantic ID and language spaces. LCCRec usually requires auxiliary tasks to align the two spaces properly (Zheng al. 2023 while Mender successfully fuses them without training auxiliary tasks. VocabEXtRND performs significantly worse than both Mender versions due to its lack of pre-trained language encoder, which requires learning the interac- tion between item history and user preferences from scratch. Based on these findings, we conclude that: (i) user preferences substantially enhance recommendation performance and (ii) representing both interaction history and preferences in natural language leads to performance improvements.
Fine- and coarse-graines steering. We observe that MenderTok consistently achieves the best perfor- mance across all datasets for fine-grained steering with relative improvements of up 70.5% to basclines. Interestingly, illustrated in Fig. | fine-grained steering naturally emerges a byproduct of training preference-based recommendation data. However, this is not the case for the Steam dataset (Fig. 5a). where we notice significant gap between the recommendation and the fine-grained steering performance, We surmise that the reason for this the fundamental difference in the respective data distribution of the Ama- zon and Steam datasets. Prior work retenstrated that data distribution an essential driving factor clicit emerging capabilites such as in-context lcarning (Chan al. 2022). We aim to verify this conjecture in future work. Finally, our results indicate that all methods struggle perform coarse-grained steering, suggesting that preference-bart based recommendation data lacks beneficial signal to facilitate its emergence.
Generally, we observe that all methods attain lower scores on history consolidation compared to the recommendation This is because the additional preferences are not necessarily related to the ground-trute item and thus add a substantial amount of noise. Furthermore, one the five user preferences provided the model contains information to identify the ground-truth item as they were matched during the benchmark generation. Therefore, the performance attained is a proxy for how well the model can identify useful preference out of set of potentially unrelated preferences. On the A Amazon subsets, Mendertok
History Consolidation.
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Published in Transactions on Machine Learning Research (07/2025)
consistently attains the highest performance, while LC-Rec attains the best results Steam. These findings suggest that preference-based methods can effectively fuse interaction history with multiple user preferences for recommendation. In Table g (Appendix E we also show results for training on history consolidation
data, demonstrating that this drastically degrades performance.
Sentiment Following. Although both Mender variants achieve the highest performance on different datasets, the overall performance on sentiment following generally around an order of magnitude lower. This result indicates that all current models struggle with sentiment following. This finding presents an interesting avenue for future research that should prioritize the development of models that can accurately identify the sentiment of user preferences and adapt their retrieval accordingly. Prior works found that there is little to no gain in incorporating negative user preferences into recommendation models (Sanner et al. 2023). Our results confirm that current systems mostly lack the ability to discern negative preferences and act accordingly. However, in the next section we show that this depends on how the negative preferences are used during training, and that it is indeod possible to obtain model that improves along this axis.
4.3 Ablation Studies
Importance of Preferences. We perform an ablation study to investigate the impact of the generated user preferences. In Fig. 8 (Appendix A.4), we provide evidence that representing items in language instead of semantic IDs leads improved rankings. Further, train Mendernok and (i) condition it only preferences;
Titles
0.08
(ii) condition it only on item descriptions; and (iii) condition it on both. We present our results for the Beauty dataset in Fig. G Our results clearly demonstrate the benefits of leveraging textual user preferences.
0.06
0.04
Ablating Training Data Mixture Study Steering emergence Training on preference-based recommendation data does not clicit steering capabilities on Steam, sentiment following capabilities any dataset. Therefore, we aim to an- swer the question whether these capabilities can be elicited by directly training on additional data for steering and sentiment following. As can be seen in Table in Appendix C we also generale training splits during benchmark generation. Hence, we can answer this question by augmenting the preference- based recommendation training set with the additional data sources and train different variants of MendeTTok- Specifically, we train different versions of Menderrok to study how seeering-speciffe data impacts performance:
0.02
0.00 MRR@10
NDCG@10
Recallga
Figure 6: Ablation study highlighting the improvement obtained via combining items and user preferences in natural language.
Mendertoo-Pos/Nees uses only positive/negative preference-item pairs
Mender combines both positive and negative preference-ite ieterenneeitem pairs
Menderra--ine//Cone CCorsse: uses only mhe/coarse-grained steering data
Mendoriaa-Fine-Coneee Coacooo uses fine- and coarse-grained stecring data
MenderTok-All trained on all data above.
When including the negative (p_,t) tuples, we simply minimize the likelihood and downweight it by a hyper- parameter, as otherwise this term dominates the loss function. We present Recall@ 10 for Beauty in Fig. 5 right, and for Steam in Appendix E
We also report Recall@5, NDCG@5, and NDCG@ 10 for all methods and evaluation axes in Table 5 (Appendix E Most importantly, coarse-graines steering and sentiment-followingg capabilitics arisc when we explicitly train the model on the respective data. Interestingly, Menderra-All significantly improves on Mendernok on all axes while maintaining performance on the recommendation axis. However, training on a cataa split in isolation improves over training on all data, i.e. Mandderoo--oaree leads to better coarso-grained stecring than Mendertak-AD but lacks sentiment following. Furthermore, sentiment- following capabilities arise only when training jointly on positive and negative data. These findings present fruitful avenue for future research combining the different. data sources.
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Scaling the language encoder. By default, use the FLAN-TS-Small encoder in experiments. To investigate the effect of scaling the encoder, we compare the performance of MenderTok with Menderrax-XT XXXT, which uses the FLAN-15-XXL encoder. The results of this experiment can be found in Table 9 (Appendix We find that the XXL variant drastically improves recommendation performance on Sports and Outdoors, fine- and coarse-grained steering on Toys and Games and Steam datasets, and sentiment following on all datasets. Furthermore, Menderrak-XX drastically improves the history consolidation axis, which indicates that the observed gap in Fig. 5a can be attributed to the language encoder. This provides compelling evidence that more capable language encoders can lead to drastic improvements the different performance axes.
5 Limitations
Generalization. The generalization capabilities of Mender are limited by the underlying generalization abilitics of the language encoder. In Table g we presented results for larger variants of the FLAN-T5 encoder which show that there are gains to using more expressive LLMs, especially for axes such as coarse-grained steering or sentiment following. Moreover, recent work (Yang et al. 2025) showed that generalization to cold- start items is limited for generative retrieval methods. Mender might alleviate this problem by combining natural language with semantic IDs. Future work should investigate whether this is indeed the case.
Computational complexity. The computational complexity of Mender is mostly restricted by the model architecture. Currently, it suffers from the quadratic complexity of the Transformer (Vaswani et al.) 2017) and the size of the language encoder that is used. Mendergmb partly alleviates this issue by pre-embedding items and user preferences, which usually results in slightly worse performance. However, there are fruitful alternatives that scale lincarly with the sequence length (Dao & Gu, 2024) and provide a constant inference cost (Beck et al. 2024). We aim to investigate such architectures for Mender in the future.
Preference Approximation. Our preference approximation pipeline is computationally expensive, as it leverages LLMs with 70B parameters. We generated around 5M user preferences for the five different datasets, which requires massive parallelization of this pipeline. A benefit. is that we rely on open source models, therefore our pipeline can be extended to new datasets, however, it is still expensive. Furthermore, extensive post-processing, which is tailored to the LLM, is required along with manual inspection to ensure high-quality user preferences. Using smaller LLMs may affect the quality of the generated preferences and in turn affect performance of Mender. Finally, we rely on the presence of user reviews, which limits the applicability of our preference approximation to certain datasets.
6 Broader Impact
Preference discerning enables dynamic steering of recommendation models based on user preferences without the need for re-training, potentially avoiding echo chambers by explicitly stating what content should be recommended. Furthermore, preference discerning can positively affect the user experience as it allows interaction with the recommendation system, fostering trust and transparency. However, potential risks include amplifying biases if preferences reflect societal prejudices leading to over-persooalliztion..
7 Conclusion
Current sequential recommendation models are limited in their personalization as they implicitly model user preferences. We propose a new paradigm, namely preference discerning, in which the sequential recommen- dation system is erplicitly conditioned on user preferences represented in natural language. To evaluate preference discerning capabilities, we present benchmark that is specifically designed to evaluate the abil- ity of sequential recommendation models to discern textual preferences along five different axes. We also propose a novel generative retrieval model, Mender, which represents items at different levels of abstraction, namely semantic IDs and natural language. Our experimental results show that Mender outperforms the state-of-tho-art models on our benchmark and can adapt to unseen preferences without any retraining. Our contributions pave the way for a new class of generative retrieval models with the ability to dynamically adapt to user preferences provided in their context.
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Supplementary Material
Fabian Paischer
ELLIS Unit, LIT Al Lab, Institute for Machine Learning, JKU Linz, Austria AI at Meta
Liu Yang
University of Wisconsin-Madisor AI at Meta
Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh" heghbalz@meta. com
Al at Meta
Reviewed on OpenReview:
https: // openreview. net/ forum? id= 74mrOdhuuT
Contents
Generative Retricval via semantic IDs
19
A.1 RQ-VAE
Transformer
20
A.3
Reproduced results
20
A 4
Additional findings
20
B Datasets
22
C Preference generation
22
Reviews to properties
25
D Benchmark design
26
D.1 Sentiment Pollowing
26
D.2 Preference Steering
26
E Additional results
28
F Manual Inspection of Preferences
29
A Generative Retrieval via semantic IDs
We provide an open source implementation of all basclines used in this work, including TIGER (Rajput et al. 2023). To facilitate reproducibility of the results reported in Rajput et. al. (2233 elaborate the implementation details follows. The training of TIGER consists of two stages: (i) training the residual quantizer (RQ-VAE) to obtain semantic IDs, and (ii) training the generative retricval model.
A.1 RQ-VAE
Training the RQ- VAE involves essential steps: (i) constructing an item embedding, and (ii) optimizing the model through residual quantization.
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Item embedding For item embedding, utilize the Sentence-T5 model [Ni et al. 2022 which is publicly available on the Hugging Face Hub (Wolf et al. 2020). We explored various sources of information to represent items and found that the optimal approach varies between datasets. For the Beauty and Sports datasets, using item descriptions led to suboptimal results due to the high noise levels present these descriptions. In contrast, ilem descriptions proved beneficial for the Toys dataset. Additionally, we leveraged other item attributes, including title, price, brand, and categories. For the Stream dataset, we utilized a broader set of attributes: title, genre, specs, tags, price, publisher, and sentiment.
Training By default, standardize the item embeddings, as this helps prevent collapse during RQ-VAE training. For training the RQ-VAE, we found that architectural changes are crucial to increase codebook coverage. Specifically, residual connections and weight decay are essential for maintaining & good separation. Our trained RQ- VAE's consistently attain a codebook coverage more than 95%. Our encoder architecture consists of four hidden layers with sizes 768, 512, 256, and 128, respectively. Each layer includes layer normalization (Ba et al., 2016), ReLU activation, and dropout (Hinton al. 2012 The decoder follows the same architecture but in reverse order, where the sum of residuals obtained via the quantization procedure is up-projected to the original dimension of 768. Following Rajput et al. (2023). we use three-level residual quantization scheme with 256 codebooks cach. We also experimented with EMA updates and resetting unused codebook entries, as in Lee et al. (2022) but did not observe any significant improvements. To evaluate the performance of our trained RQ-VAFs, we rely on metrics such as reconstruction error, codebook coverage, and downstream task performance.
A.2 Transformer
Following Rajput et al. (2023) we instantiate the generative model with the T5 architecture (Ralfel et al. 2020). Next, we investigate the design choices that underlie this approach, as introduced by Rajput et al. (2023 and discuss their utility.
Training sequences To construct the training sequences, Rajput et al. (2023) limit the number of items in a user sequence to at most 20. This can be implemented by taking the first, the last, or all items within a sliding window of up to 20 items. We experimented with each of these approaches and found that using the most recent 20 items in a user sequence generally yields improved performance. Unlike prior sequential rocommendation systems, which require at least one item in sequence predict the next item (Kang & McAuley 2018 Zhou al.] 2020),, TIGER uses a user embedding trained alongside the item embeddings. Therefore, we typically use the first item in sequence for training well.
Model architecture.
Decoding Another crucial aspect of the generative retrieval pipeline is the decoding process. As noted in Rajput et al. (2023), the generation of valid semantic IDs is not guaranteed. To mitigate this issue, we track the number invalid semantic IDs produced during decoding. We find that this number typically quite low. Nevertheless, to further improve the accuracy of our retrieval results, we employ filtering remove invalid IDs and increase the beam size to be 30, which is larger than the final retrieval set.
A.3 Reproduced results
In Table we compare the results of our reproduced version of TIGER with those reported in Rajput et al. (2023) Our results closely match those reported in RRaput et al. 2023 for the Sports and Beauty datasets, but we observe significant gap on the Toys dataset. In particular, our trained models achieve substantially higher Recall@10 scores on the Beauty dataset. Furthermore, we find that the disparity is more pronounced for NDCG than for Recall, suggesting that while the retrieved candidate items are similar, our models' ranking performance is slightly worse.
A.4 Additional findings
Beyond the experiments discussed above, we conducted further investigations into the TIGER framework, yielding the following key insights.
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000
TIGER-UserEmb
TIGER
0.07
0.06
Number of Mazches
0.05
300
0.03
0.02
100
0.00
Number of Items in label bucket
MRR@10
NDCG@10
Recall 10
Figure Left: Number of correctly retrieved test items for TIGER on the Beauty subset of the Amazon review dataset. Right: Performance comparison of TIGER with user embedding without user embedding (TIGER) the Beauty dlttaset.
and
Table 2:
Reproduced results for our open-source implementation of TIGER RRjput et al. 2023
Sports and Outdoors
Beauty
Toys and Games
Methods
Recall Q5
NDCG
Recall NDCG
Recall
NDCG
Recall
NDCG
Recall
NDCG
Recall @10
NDCG
455
310
@10
K5
(55
@10
Q10
&55
@5
&10
P5
Geng et al.
2022
0,0061
0.0041
0.0095
0.0052 0.0163
0.0107
0.0254 0.0136 0.0070 0.0050
0,0121
0.0066
Caser Vang & Wang
2018
0.0116
0.0072
0.0194
0.0097
0.0205
0.0131
0.0347
0.0176 0.0166
0.0107
0.0270
0.0141
HGN
Ma ct al. 2011
0.0189 0.0120 0.0313 0.0159 0.0325 0.0206 0.0512 0.0266 0.0321 0.0221 0.0497 0.0277
GRU-Roc Hidasi et a.
20166
0.0129 0.0086 0.0204 0,0110 0.0164
0,0099
0.0283 0.0137 0.0097 0.0059
0,0176 0.0084
BERTARec Sinn al.
2019
0.0115 0.0075 0.0191 0.0099
0.0203 0.0124
0.0347 0.0170 0.0116 0.0071 0.0203 0.0099
FDSA Zhang et al. 2019b
0.0182 0.0122 0.0288 0.0156 0.0267 0.0163 0.0407 0.0208 0.0228 0.0140 0.0881 0.0189
SASRec Kang & McAuley
20118
0,0233
0.0154 0.0350
0,0192
0.0387
0.0249
0.0605 0,0318 0.0463 0.0306
0,0675 0.0374
5 Rec Zhou al. 20200
0.0251
0.0161 0.0385
0.0204 0.0387
0.0244
0.0647
0.0327 0.0443
0.0294
0.0700 0.0376
TIGER (Rajput et al.
2023)
0.0264
0.0181 0.0400
0,0225
0.0454
0.0321
0.0648
0,0384
0.0521
0.0371
0.0712
0.0432
TIGER (Ours)
0.0249 0.0158 0.0377
0.0199
0.0431
0.0275
0.0681
0.0356
0.0375
0.0238
0.0600
0.0311
TIGER exhibits superior performance shorter sequences, shown in Fig. 7 (left).
The inclusion of user embeddings in TIGER does not yield any significant benefits to downstream performance, as illustrated in Fig. 7 (right).
Representing the interaction history in natural language leads to improved ranking performance,
demonstrated in Fig.
TIGER Works Better on Shorter Sequences. As shown in Fig. (left), TIGER performs significantly better on shorter sequences than on longer ones. The x-axis represents the number of items per test sequence, which is least due the 5-core user and item filtering appliod. Further, the maximum number of items per sequence capped 19, as we limit the maximum sequences length to 20, following (Rajput et al., 2023). This results in maximum sequence length of 19 items, where the task is to predict the 20th item. The y-axis shows the number of matches. Notably, TIGER's performance is substantially better on shorter sequences than on longer ones. However, the number of matches increases again for the longest sequences, although it remains considerably lower than for shorter sequences.
User Embedding. Rajput et al. (2023) employ user embedding selected based on hashing. However, is unclear whether this approach offers any advantages, as the number of user embeddings suggested by (Rajput et al. (022) often results in numerous collisions in practice. To investigate this, we conduct experiment
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TIGER
LIGER
Coarse
Granular
Defoult
0.07
0.06
0.08
0.05
0.06
0.03
T
0.02
0.00
0.00
MRR@10
NDCG@10
Recall@10
MAR@10
NDCG@10
Recaiig 10
Performance comparison between TIGER and LIGER on the Beauty subset of the Amazon review datasct. Both models predict semantic IDs, but differ in their input reprosentation: LIGER encodes past items natural language descriptions, while TIGER represents them semantic IDs. Right:
Figure 8: Left:
in which we remove the user embedding entirely. As shown in Fig. 7 (middle), we do not observe a significant drop in performance. This suggests that user embedding does not provide any notable benefits.
History Compression via Natural Language. We conduct an additional study which we represent the past. interaction history in text and initialize the TIGER encoder with a small FLAN-T5 encoder (Chung ett al. 2024). This approach reminiscent of history compression via language models (Paischer et al.) 2022 HELM). We refer to this variant LIGER (Language-TIGI) baseline TIGER in Fig. left. The results show that while there is significant difference in Recall, LIGER yields notable improvement in NDCG metrics. This suggests that compressing interaction history using natural language generally enhances the model's ranking capabilities.
and compare its performance with the
B Datasets
We consider two publicly available datasets for sequential recommendation: Amazon review dataset Nii et al. (219)) and Steam (Kang & McAuley 2018). To preprocess these datasets, we apply 5-core filtering criterion, removing users with fewer than five interactions and items that appear less than five times. The statistics of the resulting dataset presented in Table Due to computational constraints, sub-sample the Steam dataset to reduce the number of user preferences generated during the preference approximation pipeline.
We also visualize the item distribution in Fig. g which shows that the three Amazon datasets follow ap- proximately the same item distribution, while for Steam the distribution differs significantly. Tn particular, the Steam dataset the number of items is in the same range as for the Amazon datasets; however, the number of users is much larger, well as the average number of actions per user. As be observed from the item distribution, there is small fraction of items that is overrepresented.
C Preference generation
In this section, we provide details on the prompting scheme used to generate user preferences from item revicws using
We provide reviews along with item-specific information to the LLM and prompt to generate set of five user preferences (see Fig. 10p. Below we present an example prompt and response for user in the Beauty subset of the Amazon reviews dataset.
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Beauty
Sports & Outdoors
6000
10000
Count
4000
5000
2000
0
5000
10000
0
5000
10000
15000
Toys & Games
Steam
30000
6000
20000
Count
4000
10000
2000
0
5000
10000
5000 Items
10000
Items
Figure 9: Data distribution the Amazon and Steam datasets.
?
Review:
Review:
"After using Vision started to get headaches and eye strain. The headset bit bulky and heavy, which doesn't help."
hours,
absolutely loving my Ray glassest Not only do they look stylish, but they're also incredibly comfortable to wear. I've worn them for hours on end without any discomfort or fatigue."
Response prefer devices prioritizing comfort and minimal eye strain
Prompt
Can you generate the preferences of the user?
Llama3
Figure 10: Schematic illustration of our preference generation pipeline. A user's 'er's reviews for items, combined with item information, are input into Llama3 as prompt to infer the user's preferences.
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Dataset statistics after user 5-core and item 5-core preprocessing. subsets of the Amazon review dataset.
Table 3:
Asterisk denotes datasets are
Dataset
#users
#items
avg. actions
/user
avg. actions /item
#actions
Beauty*
22,363
12,101
8.8764
16.403
198,502
Toys and Games*
19,412
11,924
8.6337
14.0554
167,597
Sports and Outdoors*
35,598
18,357
8.3245
16. 14.1430
296,337
Yelp
19,855
14,540
10.4279
14.2387
207,045
Steam
47,761
10,403
12.354
54.6549
599,620
Table 4: Statistics for generated preferences for the different datascts. For pos/neg and fine/coarse we show mumber of samples in the format /val/test split.
Benchmark
#preferences
#positive
#negative
pos/neg
cine/caase
Beaudy
992,510
708,706
283,804
17,81 17,811/3,67//3///
24,114/16,700 702////559 15,956
Toys and Games
837,982
645,696
192,289
11,513/2.342/2,500
23,730/ 11,998 0166/14,950 1149950
Sports and Outdoors Steam
1,481,685
1,075,679
406,006
(0,552/25,772 25,188
2,026,225
1,495,931
530,294
31.505/7..68/8.498
Instruction:
Here is list of items user bought along with their respective reviews json format: } Your task is to gemerate list of up five search instructions that reflect the user's preferences based their reviews. Be specific about what the likes, does not like, and should be avoided. Do not mention brands certain products. Return json file containing the search instructions with the key "instructions. Keep the instructions simple, short and concise, and do NOT include comments on delivery time or pricing.
Parsed response:
[*Search for nail polish with shimmer finish', 'Look for products with vebrant, bold colors', 'Avosd products that require base coat for optimal results', 'Prsoritize products with high-quality, long-lasting formula', 'Opt for products with easy, smooth application')
After generation, apply an exhaustive postprocessing step to ensure that every user-item pair is associated with exactly five user preferences. In Table 4 we show the statistics after our preference generation pipeline for the different datasets.
Granularity of preferences. We also investigate whether the granularity user preferences affects the model's ability adhere them. We experiment with various prompts elicit preferences different levels of granularity, ranging from coarser, higher-level user preferences to fino-grained preferences that include detailed descriptions of products and brands. To obtain more abstract user preferences, we slightly modify the prompt as follows. We show example for the same user below.
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Instruction:
Here is list of items user bought along with their respective reviews in json format: Your task is to generate a list of up to five search instructions that summarizes the user's high-level preferences based on their reviews. Be specific on what the user does not like and should be avoided. Do not mention brands or certain products. Return json file containing the search instructions with the key "instructions. Keep the instructions simple, short and concise, and do NOT include comments on delivery time or pricing. Parsed response:
('Look for products with vsbrant colors', 'Prioritize products with high-qualsty fenishes', 'Opt for products that easy apply', 'Consider products that suitable for miltiple (e.g., fingers)', 'Avoid products that regusre addstional base coats']
Finally, to obtain more fine-grained user preferences, we slightly modily the prompt again to permit the inclusion of specific brands and item names in the LLama response. As shown below, the LLama indeed mentions brands when given the explicit instruction that mentioning item names brands is acceptable.
Instruction:
Here is a items user bought along with their respective reviews json format: ) Your task is generate list up five search instructions that reflect the user's preferences based on their reviews. Be specific about what the user likes, does not like, and should be avoided. It is okay mention brands certain products. Return json file containing the search instructions with the key 'instructions. Keep the instructions simple, short and concise, and do NOT include comments on delivery time or pricing.
Parsed response:
["Search for nail polishes with ssmélar shimmering effects to OPI's Simmer and Sh Shimmer", *Prioritšze products with vibrant, long-lasting colors that look great on toes', "Look for mosl polish brands that offer a usde range of colors, ssmslar to OPI's Burlesque co collection", 'Avoed products that regusre for optimal results', 'Opt for nail polishes with smooth, easy-to-apply formula']
In practice, we found that varying the granularity usually leads to worse performance (see Fig. g right). This suggests that the user preferences obtained through our default prompting scheme contain sufficient information to effectively steer the model.
C.1 Reviews to properties
In addition to the title and description of the items, the collective reviews of items offer a wealth of information about their properties. However, with hundreds of users potentially reviewing a single item, each contributing multiple paragraphs, extracting relevant information can be challenging. To improve the signal-to-noise ratio, we prompt LLaMA-33-7BBIInstruct to extract and condense the properties of the item from the corpus of user reviews. Specifically, we instruct the LLM to provide concise objective descriptions, excluding subjective opinions. In the following, illustrate this approach with example of extracting properties from reviews of froggy pool in the Amazon Toys and Games dataset.
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Instruction:
Your task is to summarize the following reviews of an item into list of item properties using keywords and phrases: } Keep your response short and concise. Only focus on objective properties of the item. Do NOT include subjective opinions or emotions. Do NOT include comments on price or delivery time. Return your response as a python list with at most 10 entries that accurately reflect the properties of the item.
Parsed response:
Inflatable pool with a frog head design, Rasnbow sunshade provides shade for the entére pool, inflatable cushions, pool holds gallons of water, Nossmum fill line is snches, Inflated poot has 28 inch diameter, Three separate valves for inflation, Easy up and take down, Sustable for children aged 1-3 years, Oushsoned inflatable base for comfort.
base consiats of multsple
Original review:
This froggy pool is great for your young children. It is 45" 39 a 27" and very confortable and safe for your chsld.
The parsed response above demonstrates that LLama3 is capable of providing a comprchensive list of char- acteristics about toys in the Amazon Toys and Games dataset. We observed modest. performance gains when providing summarized item properties along with item titles and preferences as input to our language- conditioned generative retrieval model. Since we were limited by the number of prompts could issue, we did not compile the item properties for all datasets. Nevertheless, we provide the prompts to facilitate future research in this direction.
D Benchmark design
In this section, we provide additional details on the creation of the various components of our benchmark.
D.1 Sentiment Following
The sentiment understanding benchmark is based on preference-item pairs and utilizes a matching mecha- nism to identify items that triggered negative reviews. This is implemented using a pre-trained sentiment classification model from Hartmann et al. (023) to classify reviews. To identify preferences, we employ a rule-based approach, as we observed that preferences can be both positive and negative simultaneously (e.g., a preference may specify liking certain items while avoiding others). Furthermore, we noticed that negative preferences consistently follow specific pattern, starting with either "Avoid", " "Eaclude", or " "No". To reduce misclassifications, consider preferences beginning with these words negative. If only one item in user sequence received negative review, pair the negative preference with that item. Otherwise, we use a matching mechanism in the Sentence-T5s space, where we match negative preference to the item whose review is closest in terms of cosine similarity. An example of the negative matching pipeline is illustrated in Fig, II This yields a set of negative preference-item pairs, enabling us to assess whether the model can recognize negative sentiment and respond accordingly. To obtain positive pairs of preferences-itemt we iterate over all negative pairs and invert the gathered preferences. Since negative instructions always start with "Avoid", "Eclude", or "No", we simply replace these words with " "Find" or "Search for" to invert them. This results in two sets: one that contains negalive preferences paired with items and another containing positive preferences paired with the same items. Finally, we assess whether the model can successfully avoid certain items while actively retrieving others.
D.2 Preference Steering
In the preference stecring scenario, we consider two distinct scenarios: fine-grained and coarse-grained pref- erence steering. The former assesses whether the model can relrieve an item very similar to the ground truth by modifying the user preference. In contrast, the latter evaluates whether the model can retrieve a distinctly different item by changing the user preference accordingly. We identify very similar item by the maximal cosine similarity in pre-trained Sentence-TT T5 embedding space. In contrast, we retrieve a very distinct item by the lowest. cosine similarity to the ground-truth item. Subsequently, we match the retrieved
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Positive
Preference 1:
AR devices prioritizing comfort and minimal eye strain.
Negative
Review:
Review:
"After using the Vision Pro for few hours, started to get headaches and eye strain. The headiset bulky heavy, which doesn't help.'
"Im absolutely loving Ray Ban glasses! Not only they look stylish, but they're also incredibly comfortable to I've wom them for hours on end without any discomfort fatigue."
Preference 2: Avoid bulky headsets.
Negative
Positive
Figure 11: Schematic illustration of our pipeline to identify the reviews that triggered negative user prefer- ences. The reviews of different items guided the LLM to generate two distinct user preferences. We perform sentiment classification on both user preferences and reviews, followed by a matching step in Sentence-Tr space to determine which negative review led to a negative user preference.
Preference 2: Avoid bulky headsets.
Invert
Preference 2: Find bulky headsets
Figure 12: Positive and negative preference-item pairs obtained after matching negative preferences to items that received negative review. We apply a rule-based inversion to generate the corresponding positive pair.
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Preference: Curved OLED monitors
Preference: devices prioritizing comfort and minimal strain.
Preference: Newest AR models.
line-grained
Preference:
Newest AR models.
Coarse-grained
Preference:
Curved OLED monitors
Figure 13: Schematic illustration of our pipeline for constructing fine- and coarso-grained preference steering. We search for very similar and dissimilar items to the ground truth item of each original item sequence and match them to user preferences (top). Then, we obtain two new sequences by exchanging the original preference with each user preferences and associated new ground truth item.
items to new user preferences, again via cosine similarity. We show visual illustration of this procedure in Fig. 13 Finally, we ensure that there is no overlap between our compiled training, validation, and test split by controlling for the matched preferences, iee if user preference was already matched a retrieved item, we associate the current item with the next most similar or distinct preference. This results in unique (preference, item) tuples for every dataset split.
E
Additional results
We provide complementary results for our ablation studies on the data mixture. In Table 5 we report Recall@5, Recall@10, NDCG@5 and NDCG@10 for the different versions Mender that are trained on different data mixes. Furthermore, we provide results for training on the Steam dataset with different data mixtures in Fig. 14 to highlight that fine- and coarse-grained steering, as well as sentiment following capabilities can be obtained on this dataset well.
In addition, we report standard deviations of our results in Table in Table 0 with the higher values colored red. The small standard deviation indicates that the improvements reported in Mender are statistically significant.
To assess the efficiency of our Mender variants, we compare the time required for training and inference well as their performance. Furthermore, add a comparison to SASRec (Kang & McAuley 2018), which is traditional sequential recommendation baseline. We present our results in Table 7 for the four datasets.
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Nen Menderiee Fine
Menderya-Comee
Menderice Menderya-
Mender Pos-Poo
Mender Mender Pos
Recommendation 0.204
Fine grained
Coarse- grained
0.174
0.146
0.075
0.079
History Consolidation
Sentiment
Figure 14: @ecall@10 for MenderTok trained different datasplits the Steam dataset, evaluated under various schemes: Recommendation, Sentiment following, Preference steering, Preference consolidation, and History consolidation.
In addition, we conduct experiment to demonstrate that training on all five generated user preferences leads lo detrimental performance. As mentioned in Section 3.5 each training sequence contains a single user preference that is matched to the target item in a pre-trainee traiieeee Sentence space. To verify that this is the best training strategy, we compare MenderTok trained on these sequences to the setup where Mendertok receives all five user preferences along with the interaction history MMenderaa--Allreeer sequences structured as [p:: Tu-1 , TT ........ iT. 16.-1 We report results in Table S They verify
that is, the training
that training on soquences [p? T ,.... i1....,, iT. -1] where -- is matched the ground truth item ir.-1
allains significantly better results than training on providing all preferences in the sequence.
Finally, conduct an experiment where exchange the language encoder of MenderTok with larger variant. By default, all experiments use the FLAN-T5-Small model (Chung et al. 2024). In Table g we provide results for a comparison to the XXL variant. We observe that drastic improvements can be obtained on certain datasets usually for tasks such as fine-grained steering, coarse-grained steering, or sentiment following. This provides evidence that, for the more language-intensitgg tasks, it beneficial to scale the language encoder. However, on Beauty, Toys and Games and Steam, there are some discrepancies, which are mainly due to the fact that fine-tune the small variant, but not the large performance. Due to computational requirements, we do not fine-tune the XXL encoder. Impressively, the XXL variants improve performance Steam datasets, even though we compare to the fine-tuned small variant. This provides compelling evidence that more capable models can lead to drastic improvements on the different performance axes. Finally, the XXL variant leads a drastic improvement on the history consolidation task on Steam, indicating that a better language understanding is required to tackle this task on the Steam dataset.
this lead to improved
fine- and coarse-grained steering on the Toys and Games and the
F Manual Inspection of Preferences
Our aim is to verify that the user preferences generated by the LLM accurately approximate the real user preferences. To this end, we conduct manual confirmation the preferences answer the following questions:
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1. Are the generated user preferences informed by the user's past interaction history?
2. Do the generated preferences accurately approximate the user's preferences?
3. Is the matched preference related to the target item?
4. Given that a user preference accurately approximates the user's preferences, is it related to the larget item?
In total, we manually inspected 440 recommendation scenarios, which is equivalent to 2200 preforences that were judged. Each scenario consists of 20 randomly sampled recommendations of one of the Beauty, Toys and Games, Sports and Outdoors, or Steam datasets. In one of such scenario, we first show the past interaction history of a random user along with their reviews. Then, the generated user preferences are displayed along with the one user preference that was matched to the ground-truth item, i.e. the next item in the sequence. Finally, we also display the ground truth item with the same information as the recommendation system would receive it. For each scenario, we answer all three aforementioned questions and provide one of three possible answers, namely (1) yes, (2) no, or (3) lack of information to tell. We now iterate over all the questions and present the corresponding findings.
Are the generated user preferences informed by the user's past interaction history? The ob- jective of introducing this question was to quantify how much of the generated preferences was actually represented in the interaction history and what amount has been hallucinated. We report the results of this first question in Fig. 15 The majority of generated user preferences are well informed by the user's interac- tion history across datasets. We found that the model occasionally generated rather generic preferences, for example, "Avoid harsh chemicals" on the Beauty dataset, although there was no mention of "harsh chemi- cals" in the reviews. Such preferences are rather generic and do not convey much information about user's preference. Furthermore, there was lack of information in some scenarios to answer the question. This can be traced back to the fact that we intentionally did not provide item descriptions, as these often contain a substantial amount of noise. As this information is hidden, we believe that it caused the small fraction of preferences that were rated as lack of info. Thus, we can conclude that the generated user preferences for the most part were informed by reviews or item-specific info, however, there is still a non-negligible amount of user preferences that can be considered hallucinated.
Do the generated preferences accurately approximate the user's preferences? The purpose of this question is to quantify whether user preferences correctly approximated. This question is crucial because it sils at the core of evaluating the quality of preferences. We report the result in Fig. 16 Again, we find that, for the most part, the preferences accurately reflect the user's preferences. The answer lack of info mcans that there is not enough information to capture the user's preferences, which is the case very little detail is given in the reviews or they are missing entirely. Fortunately, this case is underrepresented. Overall, we can conclude that the approximation of user preferences via our preference approximation strategy yields high-quality preferences that accurately reflect the user's preferences.
Is the matched preference related to the target item? After we have established the quality of the preferences, it is imperative to also evaluate our matching of preferences to larget items for preference-based recommendation. The reason we conduct this matching is to provide the model with a useful training signal. This is imperative as we observed that simply using all preferences for training leads to detrimental perfor- mance (see Table D.. We report the results for this question in Fig. 17 Interestingly, the fraction of correctly matched preferences significantly lower compared reasons for this can be two fold, (i) it can be that the target item is entirely unrelated to the past interac- tion history, (ii), the matching mechanism is suboptimal. The former case reflects the inherent aleatoric uncertainty of the sequential recommendation task, as oftentimes the target item is simply not related to previously acquired purchases. This shortcoming cannot be alleviated. However, the latter can be tackled by potentially more expressive embedding models or LLMs that can be used to match preferences to the target item. Finally, the lack of info category represents cases where the information about the target item is too little, i.c., no description or item title given. Overall, we can conclude that even though we demonstratod significant performance gains resulting from training on the matched preferences, it could likely be further improved by improving the matching of preferences to items.
the number of correctly generated preferences. The
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Beauty
Sports Outdoors
Toys & Games
Steam
info
7.1%
15.0%
9.2%
16.2%
60. 60.8%
82.1%
78.0%
81.7%
Figure 15: Manual inspection results for the question "Are the generated user preferences informed by the user's past interaction history?" for the four different datasets used for approximating user preferences.
Beauty
Sports & Outdoors
Toys & Games
Steam
yes
yes
7.1%
16. 0%
6.2%
lack Info
8.3%
3%
21.0%
63.00 ...0%
71.2%
73.3%
87.1%
Figure 16: Manual inspection results for the question "Do the generated preferences accurately approximate the user's preferences?" for the four different datascts used for approximating user preferences.
Beauty
Sports & Outdoors
Toys & Games
Steam
yes
15.0%
23.0 0.0%%
28.3%
56 4%
28.60 6%
60.0%
34.0%
Figure 17: Manual inspection results for the question *Is the matched preference related to the larget item?" for the four different datasets used for approximating user preferences.
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Given that user preference accurately approximates the user's preferences, is it related to the target item? We can obtain estimate on the underlying aleatoric uncertainty of the task by evaluating whether accurate preferences are related to the target item. In particular, we consider the cases where Q2 was answered yes and visualize the three categorics for Q3 (see Fig. 18p. In other words, we look at correctly approximated preferences and ask what fraction of them related to the target item. If Q2 is answered with yes, then we expect the matching perform well there semantic relation the target item. However, if there is still no relation to the target item, that is, Q3 is answered with no, then can infer that this is due to the inherent uncertainty of the task. Interestingly, 50-70% of the correctly approximated preferences are related to the target item. This provides us with an empirical upper bound on the maximum performance that can be obtained on the sequential recommendation task, i.e. the maximum Recall that can be obtained is in the range of 0.5-0.7, depending on the dataset.
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Table 5: Performance for different versions of Mender trained different data mixtures for all evaluation axes the Beauty and Steam datasets. We report average performance across three random seeds.
Beauty
Steam
Methods
Recall @5
NDCG @5
Recall @10
NDCG @10
Recall @5
NDCG @5
Recall @10
NDCG @10
Recommendation
Menderrok
0.0605
0.0401
0.0937
0.0508
0.1682
0.1441
0.2037
0.1555
Menderrok -Pos
0.0553
0.0371
0.0840
0.0463
0.1667
0.1429
0.2004
0.1538
Monderrot-Neg
0.0598
0.0394
0.0917
0.0497
0.1646
0.1410
0.1983
0.1519
Mender
0.0491
0.0321
0.0778
0.0413
0.1647
0.1416
0.1979
0.1523
Mender
0.0591
0.0383
0.0918
0.0487
0. 0.1666
0.1428
0.2005
0. 0.1338
Menderrole- Coarse
0.0601
0.0392
0.0924
0.0496
0. 1682
0.1440
0.2018
0.1549
Menderrok-l
Fine-Coaras Cattrrrr---i---aaaaeeee
0.0570
0.0366
0.0893
0.0470
1633
0.1424
0.2007
0.1535
Monder All
0.0529
0.0337
0.0838
0.0436
0.1634
0.1400
0.1969
0.1508
Fino-grained steering
MenderTok
0.0534
0.0344
0.0844
0.0444
0.0218
0.0137
0.0357
0.0182
Mender -Pss
0.0501
0.0321
0.0791
0.0414
0.0217
0.0137
0.0343
0.0177
Menderrok-Nee
0.0500
0.0323
0.0803
0.0420
0.0196
0.0124
0.0318
0.0163
Meenerrru-PPos-- Pos-Neg
0.0513
0.0333
0.0791
0.0423
0.0211
0.0131
0.0344
0.0173
Menderroa-Pinn
0.2476
0. 1680
0.3475
0.2002
0.0829
0.0538
0.1234
0.0668
Menderrot-Connee Coarse
0.1483
0.0981
0.2212
0.1215
0.0395
0.0244
0.0652
0.0327
Mender Seta---iie-Craaeee
0.2781
0.1885
0.3861
0.2234
0.0985
0.0643
0.1459
0.0795
Mendernoa-All
0.2676
0. 0808
0.3750
0.2148
0.0903
0.0601
0.1338
0.0741
Coarse-grained
steering
Menderrole
0.0094
0.0059
0.0161
0.0080
0.0045
0.0028
0.0085
0.0041
Menderroa-PPe
0.0098
0.0062
0.0163
0.0083
0.0047
0.0029
0.0079
0.0040
MenderTok-Ne
0.0063
0.0039
0.0117
0.0056
0.0041
0.0027
0.0072
0.0036
Senderrma-Po--Vee
0.0095
0.0061
0.0169
0.0084
0.0050
0.0031
0.0083
0.0041
Menderrog-Finm
0,1005
0.0655
0.1494
0.0813
0.0272
0.0175
0.0691
0.0304
Menderrok- Coarse
0.3028
0.2631
0.3541
0.2797
0.0953
0.0485
0.1385
0.0624
0.3525
0.2710
0.4413
0.2999
0.1 0.1403
0. 1052
0.1741
0.1163
MenderToa-Al
0.3294
0.2779
0.3885
0.2970
0.1063
0.0696
0.1495
0.0839
Sentiment following
Menderrok
0.0043
0.0053
0.0084
0.0110
Menderrog-Pos
0.0113
0.0140
0.0123
0.0134
Menderna--Neg
0.0000
0.0000
0.0000
0.0000
Menderrra-Poo--Ngg
0.0268
0.0414
0.0637
0.0787
Mendernok FFnne
0.0046
0.0075
0. 0.0080
0.0112
Mender Coarse
0.0067
0.0089
0.0088
0.0184
Menderrot- Fine-CFarsse
0.0057
0.0083
0.0053
0.0081
Menderno--AN
0.0440
0.0635
0.0184
0.0256
History consolidation
Menderrok
0.0457
0.0304
0.0720
0.0388
0.0490
0.0317
0.0745
0.0399
Menderrac-Pss Pos
0.0405
0.0272
0.0632
0.0344
0.0490
0.0331
0.0704
0.0400
Menderrat-Nes,
0.0460
0.0301
0.0714
0.0383
0.0448
0.0288
0.0667
0.0359
Menderrow-Pos-Nes
0.0359
0.0233
0.0581
0.0305
0.0440
0.0293
0.0649
0.0360
Mendernaa-Finn Fine
0.0418
0.0270
0.0657
0.0346
0.0492
0.0333
0.0730
0.0410
Menderrok- Coceeerrre-CCrrseeee Coarse
0.0436
0.0284
0.0682
0.0363
0.0495
0.0331
0.0728
0.0406
Mender Fine- Coarsee
0,0399
0.0254
0.0636
0.0331
0.0517
0.0355
0.0753
0.0430
Menderrol-
0.0379
0.0236
0.0607
0.0309
0.0506
0.0349
0.0713
0.0416
33

---

Published in Transactions on Machine Learning Research (07 (025))
Table 6: Standard deviation for all methods on all evaluation axes for all datasets trained on recommendation data across three random seeds.
Sports and Outdoors
Beauty
Toys and Games
Steam
Methods
Rocall NDCG NDC
Rocall Recall NDCG ReCa Recall NDCG
NDCG
@5
@5
&10
605
R5
810
@10
655
85
@10
@10
@5
A5
@10
@10
Recommendation
TIGER
0.0009 0000 0.0006
0.0005
0.0010 O. 0.0009 0.0012
0.0009
0.0008
0.0005
0.0004
0.0004
0.0015
0.0014
0.0008 0.0012
VocabExtRNND
0. 0.0002 O. 0.0001 0.0002 0.0000 0.0020 0.0017 0.0034 0.0022 0.0005 0.0006 0.0006 0.0006 0.0006 0.0002 0.0015 0.0001
LC-Ree
0.0021 0.0014 0.0027 0.0016 0.0010 0.0007 0.0006 0.0006 0.0010 O. 0.0009 0.0015 0,0010 0.0014 0. 0019 0.0013 0.0019
Menderamb
0.0011 O. 0005 0.0017 0.0007 0.0007 0.0007 0.0017 0.0010 0.0015 0.0010 0.0023 0.0012 0.0035 O. 000000 0.0040 0.0031
Menderr.k
0.0007 0.0005 0.0005 0.0004 0.0004 0.0001 0.0012 0.0002 0.0019 0.0011 0.0022 0.0012 0.0006 0.0004 O. 0.0004 0.0003
Fine- granggrainedd steoring
TIGER
0.0006 0.0004 0.0006 0.0004
0,00410 0.0024 0.0065 0.0032 0.0010 0.0006 0.0032 0.001 0.0005 O. 0.0003 0.00TO 0.0004
VocabExtRRND
0.0007 0000 0.0006 0.0005 0.00 0.0004 0.0019 0.0009 0.00 0.0004 0.0U10 0.0004 0.00TO O 0.0005 0. 0.0004
LC-Rec
0.0034 O. 0022 0.0054 0.0028| O. 0,0009 O. 0.0004 0.0018 201800 0.0007 0,0016 0.0010 O. 0.0024 0,0012 0.0014 O. 0000 O. 0.0020 0.0007
Mendergmb 0. 0,00 0005 0.0013 0.0007 0.0017 0.0013 0L0015 0.0012 0.0020 O. 0.0017 0.0015 0.001 5 0.0024 4 0L0U39 0.001
Menderrok
0.0004 0.0000 0 0.0003 0.0012 0.0007 0.001O 0.0006 0.0S 0.0004 0.OU1O 0.0004 0,00U5 0.0003 4.0004 0.0003
Coarse-grainee steering
TIGER
0.0000 O. 0000 0.0000
0.0000 0.0001 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001
0.0001 0.0001 0.0001 O. 0.0002 0.0001
VocabExtquND
0.0001 0.0000 0.0001 0.0000 0.0003 0.0002 0.0002 0.0000 0.0004 0.0003 0.0002 0.0002 O. 0.0002 O. 0000 0.0004 0.0001
LC-Rec
0.0005 0003 0.0008 O. 0.0004 0. O. 0.0003 0.0012 0.0005 0.0007
0.0005 0.0009 0.0005 0.0005 O. 0.0004
0008 0.0004
Mendergmb
0.0000 O. 0000 0.0004 0.0001 0. 0.000S O. 0.0005 0.0000 0.0002 0.0009 O. 0.0006 0.0009 0.0005 0.0005 0. 0.0010 O. 0.0003
Menderrak
0.0002 0.0001 0.0005 0.0002 0.0015 0.0011 0.0017 0.0011 0.0003 O. 0.0002 0.0009 0.0004 O. 0.0005 O. 0.0003 0.0002 0.0001
Sentiment following
TIGER
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
VocabExtRRND
0.0000
0.0000
0.0012
0.0005
0.0000
0.0000
0.0029
0.0010
LC-Ree
0.0003
0.0007
0.0006
0.0012
0.0003
0.0007
0.0016
0.0014
Mendergumb
0.0001
0.0001
0.0003
0.0007
0.0002
0.0005
0.0003
0.0020
Menderrok
0.0011
0.0012
0.0014
0.0003
0.0000
0.0002
0.0012
0.0014
History consolidation
TIGER
0.0000 0.0000 0.0000 O. 0.0000 0.0000 0.0000 O. 0.0000 0.0000 0.0000 O. 0.0000 0.0000 0.0000 O. 0,0000 0,0000 0.0000 O. 0,0000
VocabExtquND
0.0001
0.0000 0.0007 0.0003 0.0017 O. 6,0016 O. 0,000 0.0017 0.0009 O. 00000 O. 0.0006 0.0007 0.0023
O. 0027 0.0088 0.0028
LC-Rec
0.0009
0000006000 0.0012 0.0007 0.0012 0.0007 0.0012 0.0007 0.000S 0.0003 0.0018 8 0.0007 0.0014 4 0. 1 O 2012 0.001 8
Menderemb
0.0011
0.0005 0.0018 O. 0.0007 0.0007 0.0003 O. 0.0005 0.0002 O. 0.0006 O. 00000 O. 0.0015 0.0007 O. 0.0003 0.0007 O 0.0006 O. 0.0008
Menderrok
0.0008 0006 0. 0.0007 O. 0.0006 0.0005 0,00000 0.0005 0.0001 0.0015 O. 0.0013 0.0014 0.0013 O. 0,0030 O. 0000 0.0038 0.0025
Beauty
Sports & Outdoors
Toys & Games
Steam
yes mg
yes no
144. 89%
lack of info
11.1%
lack info
26 1%
28. 28.1%
59.00 0.0%%
26.2%
50.00 8%
38.1%
63.2%
yes
lack Info
Info
Figure 18: Manual inspection results for the question 'Given that a user preference accurately approximates the user's preferences, related the target item?" for the four different datascts used for approximating user preferences.
34

---

Published in Transactions on Machine Learning Research (07 (025)
Table 7: Performance, training time and inference time A100 GPU for MenderEmb; Menderrok, and traditional sequential recommendation system SASRec (Kang & McAuley 2018) on Beauty, Sports and Outdoors, Toys and Games, and Steam.
Method
Dataset
Train time
Inference time
NDGC@10
Recall@10 10
Beauty
293min
8ms
0.0227 + 0.0004
0.0528
0.0006
Sports and Outdoors
447min
9mns
0.0118 + 0.0002
0.0271 + 0.0005
SASRec
Toys and Games
280min
5ms
0.0267 = 0.0002
0.0615 = 0.0002
Steam
280min
5ms
0.1469 = 0.0002
0.1781 = 0.0004
Beauty
127min
453ms
0.0405 + 0.001
0.0755 + 0.0017
Sports and Outdoors
374min
194ms
0.0215 +: 0.0007
0.0394 + 0.0017
Mender
Toys and Games
239min
178ms
0.0342 = 0.0015
0.0653
0.0015
Steam
23lmin
179ms
0.123 0.0031
0.182 0.004
Beauty
2324min
562ms
0.0508 +: 0.0002
0.0937 + 0.0012
Sports and Outdoors
2350min
210ms
0.0234 + 0.0004
0.0427 + 0.0005
Mendertok
Toys and Games
1021min
227ms
0.0432 + 0.0012
0.0799 + 0.0022
Steam
2330min
222ms
0.156
0.0003
0.204 E 0.0004
Table 8: Performance of MenderTok when being trained on the single matched preference compared to training on all five generated user preferences on the Amazon datasets. For sentiment following we report m@10 instead of Recal1@11.
Beauty
Sports
Toys
Methods
Recall @10
NDCG @10
Recall @10
NDCG @10
Recall @10
NDCG @10
Recommendation
Mendernak
0.0937
0.0508
0.0427
0.0234
0.0799 0.0074
0.0432
Maadeerpa-AllPeeee
0.0131
0.0066
0.0063
0.0037
0.0039
Fine-graiee Fine-grained stecring
Menderrok
0.0844
0.0444
0.0324
0.0159
0.0639
0.0321
Menderpa--AlPree
0.0014
0.0006
0.0009
0.0004
0.0018
0.0009
Coarse-grained stecring
Menderrok
0.0161
0.0080
0.0045
0.0021
0.0060
0.0029
Menderma-AlIPPeee
0.0006
0.0002
0.0003
0.0002
0.0006
0.0003
Sentiment following
MenderTak
0.0053
0.0042
0.0017
Meldeeroa-AllPress AllPrefs
0.0008
0.0001
0.0005
History consolidation
Mendermak
0.0720
0.0388
0.0345
0.0187
0.0700
0.0377
Menderpoo-AIPPe AllPrefss
0.0089
0.0041
0.0063
0.0038
0.0046
0.0025
35

---

Published in Transactions on Machine Learning Research (07/202) 2025)
Table 9: Performance for MenderTok compared to Mender for all datasets trained on recommen- dation data. We report average performance across three random seeds. For sentiment, following report m@k for & E (5, 10) instead of Recall@k.
Sports and Outdoors
Beauty
Toys and Games
Steam
Methods
Recall NDCG Recall NDCG
Recall NDCG Recall NDCG Recall NDCG Rocall NDCG Recall NDCG
Recall NDCG
35
45
@10
@10
05
@5
@10
a10
005
855
310
@10
355
05
@10
310
Recommendation
Menderrak
0.0282 0.0188
0.0427 0.0234
0.0605
0.0401 0.0937 0.0508 0.0533 0.0346 0.0799
0.0432
0.1682
0.1441
0.2037
0.1555
Mender XXL 0.0302 0.0201
0.0444
0.0247
0. 0523 0.0341
0.0802 0.0431 0.0466
0.0307
0.0691
0.0380
0.1702
0. 471
0.2033
0.1579
Fine-grained steering
Menderrok
0.0190 0.01 16 0.0324
0.0159
0.0534
0.0344
0.0844 0.0444 0.0378 0.0237
0.0639
0.0321 0.0218 0.0137
0.0357 0.0182
Mondernat-
TXXLL 0.0338 0.0206
0.0551
0.0274
0.0495 0.0319
0.0787 0.0412 0.0:28 O 0.0264 0.0681 0.0347 o 0246 0.0157 0.0394
0.0204
granne steering
Mender Tok
0.0023
0.0013
0.0045
0.0021
0. 0094
0.0059 0.0161 0.0080 0.0032 0.0020
0.0060
0.0029 O. 4.0045 0.0028 0.0085
0.0041
Mender
XXL 0.0096 0.0058
0.0172
0.0082
0.0104 0.0062
0.0184 0.0087 0.0086 0.0053
0.01440 0.0070
0.0048 0.0029
0.0091
0.00
Sentiment following
Menderrak 0.0035
0.0042
0.0043
0.0053
0.0016
0.0017
0.0084
0.0110
Menderrat- XXL 0.0044
0.0064
0.0076
0.0103
0.0020
0.0048
0.0135
0.0197
History consolidation
Menderrok 0.0234 0.0151 0. 0.0345
0.0187 0. 0457 0.0304 0.0720 0.0388 0.0467 0. 0.0302 O. 0.0700
0.0377
0.0490
0.0317 0.0745
0.0399
Mender X.L0 0.0144 0. 0.0334 0.01 80 0. 0362 0.0235 0. 0.0574
0.0303 0.0383
0.0253
0.0582 0.0317 0. 1225 0. 1015 O. 1414
109
36


Reviews:
[{"review_id": "YPUeEdLOfZ", "reviewer": "Reviewer_R3DY", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. This paper discusses the important issue of how the recommendation system can obtain user preferences more effectively. The new paradigm of preference recognition proposed in this paper has room for application.\n2. The evaluation indicators proposed in this paper are detailed. These metrics take into account the varying degrees of change in user preferences, as well as the likes and dislikes of users.\n3. The paper has carried out sufficiently detailed experiments. It has been tested on multiple datasets on Amazon and Steam and on multiple platforms to prove its effectiveness.\n4. The part of the paper on how LLM can extract user preferences is valuable. This paper explains the influence of the thickness and granularity of extraction preferences on the experimental results, and answers the possible questions in the main text. In addition, the paper also explains how the method used to format the user evaluation, which can provide reference for the future work.\n\nWeaknesses:\n1. In section 3.5, when the authors propose new measures, they state that \"Since $p\\_u^{(t-1)}$ is generated based only on information about past items, there is no information leak...\". This statement requires further checking. First, in the formula for calculating $p\\_u^{(t-1)}$, $i\\_u^{t}$is actually the input. Second, in practical application, it seems impractical to take $i_u^{t}$ as the input, so the rationality of this metric may require further discussion.\n2. Further explanation and experiments are suggested to determine the positive effect of user preference information proposed in this paper. The effect of user preferences was mentioned in the smile experiment conducted in the paper, but from the experimental results, it seems that its effect alone is lower than the effect of object description alone.\n3. The LLM used in this paper was slightly large (LLaMA-3-70B-Instruct) and may not be widely applied to the community. It is suggested that the results of experiments conducted on smaller models or distillation models can be supplemented.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a new preference recognition paradigm, which uses LLM to summarize the user's historical evaluation and historical interactive item information into user preferences, and then guides the multimodal recommendation model. Based on this paradigm, this paper proposes five subtasks to measure the ability of multimodal recommendation models to recognize user preferences. Then, it proposes a new approach, mender, which is a multimodal model integrating semantic id and language preferences, and achieves the most advanced performance in benchmarking.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to “Strengths And Weaknesses”.", "broader_impact_concerns": "This work has no moral or ethical concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "jyTr5XniI8", "reviewer": "Reviewer_Kc2f", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\nPractical relevance: I appreciate that the authors tackle a real problem - current recommendation models can't adapt to changing user preferences without costly retraining.\nComprehensive evaluation: The benchmark they created tests multiple capabilities rather than just focusing on standard recommendation metrics. This gives a much better picture of how these models actually behave.\nSolid empirical results: Mender clearly outperforms baselines on most evaluation axes. The improvements look substantial enough to matter in practice.\nAblation studies: Fig. 5 and the associated discussion were particularly useful - I liked seeing how different training data combinations affect the model's capabilities.\nManual verification: Their human evaluation of preference quality (Appendix F) strengthens confidence in the approach - finding that ~75% of generated preferences correctly reflect user intentions is important validation.\nWeaknesses:\nConfusing introduction: I had to re-read the first two pages several times to understand what problem they're solving. The authors jump into their solution without properly setting up the problem first.\nMissing background: Section 3 drops me right into the methodology without any context. As someone not deeply familiar with generative retrieval, I struggled to follow their technical approach without more background.\nVague preference generation process: Algorithm 1 doesn't tell me much about how preferences are actually generated. What prompt do they use? How do they ensure quality? The explanation is too thin.\nLimited dataset scope: I wonder how well this would work beyond e-commerce. Would it generalize to music, video, or news recommendation where user preferences might be more abstract?\nEfficiency concerns: While they mention MenderEmb is 5x faster than MenderTok, there's little discussion about the computational cost of their approach. Using LLMs for preference extraction must have significant overhead - is this practical?\nMissing comparisons: They focus almost exclusively on comparing against generative retrieval methods. I would've liked to see how this compares to traditional recommendation approaches too.\nImplementation details buried: Important details about model architecture are scattered between the main text and appendices, making it hard to get a complete picture of their approach.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces \"preference discerning\" as a novel paradigm for sequential recommendation systems. I found their approach interesting - they use a two-stage process where they first extract user preferences using LLMs, then explicitly condition a recommendation model on these preferences during inference.\n\nThe main contributions as I see them are:\n\nThey propose a way to dynamically adapt recommendations based on changing user preferences without retraining models, which addresses a practical limitation of current systems.\nThey've developed a benchmark that evaluates preference discerning across five different aspects: preference-based recommendation, sentiment following, fine/coarse-grained steering, and history consolidation.\nThey introduce Mender (and its variants MenderTok/MenderEmb), which combines pre-trained language encoders with generative retrieval. The architecture uses cross-attention to fuse language understanding with collaborative filtering.\nTheir experiments on Amazon reviews and Steam datasets show meaningful improvements over existing methods, particularly in preference-based recommendation where they achieve up to 45% relative improvement.\nThe core idea - that we can represent user preferences in natural language and use them to steer recommendations - strikes me as a promising direction, especially since this provides a more interpretable interface for recommendation systems.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "Rewrite the introduction: The paper desperately needs a clearer problem statement before diving into the solution. Why should I care about preference discerning? What specific limitations does it address? Start with the problem, then introduce your approach.\nAdd background section: Before Section 3, include background on sequential recommendation, generative retrieval, and why existing approaches struggle with adapting to user preferences. This would help readers understand where your work fits.\nClarify preference generation: Provide concrete examples of how preferences are extracted from user data. Show actual prompts used with the LLM and example outputs for a few representative cases. This is crucial for reproducibility.\nCompare with non-generative methods: Include at least a brief comparison with traditional recommendation approaches to situate your work in the broader landscape. SASRec appears in an appendix table but deserves discussion in the main text.\n\nRecommended Changes:\nBetter discuss limitations: The limitations section (Section 5) feels like an afterthought. Expand on potential issues with privacy, generalizability, and computational requirements.\nInclude worked examples: A concrete walk-through of how a specific user's preferences are extracted and used would make your approach much more tangible.\nImprove figures: Figure 4 packs in too much information without enough explanation. Consider breaking this down or adding annotations to help readers interpret the results.\nAddress computational costs: Discuss the practical implications of using LLMs for preference extraction. Is this feasible at scale? What are the trade-offs?\nConsolidate implementation details: Pull important architecture details from the appendices into the main text so readers don't have to jump back and forth.\nAdd failure analysis: Where does your approach struggle? Are there particular types of preferences or domains where it doesn't work well?\nClarify training vs. inference use: I got confused about whether user preferences are used just during training or also at inference time. Make this distinction clearer throughout.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Z0Fi9MhCpD", "reviewer": "Reviewer_uuYr", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength\n- The paper is overall clearly written and easy to read.\n- The proposed method is demonstrated to be effective on many datasets.\n- Extensive model studies are conducted.\n\n\nWeakness\n- Some of the model design is not clearly mentioned. For example, from Section 3.2, it seems that the author is making a multimodal recommendation. However, there is no explicit explanation of what multimodal large language models are adopted as the backbone.\n- Some illustrations are not clear, e.g., in Algorithm 1, many parameters are not defined.\n- The presentation of the results is hard to read.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper studies the problem of sequential generative recommendation. The authors propose a new paradigm called preference discerning, where the recommendation model explicitly conditions on user preferences in natural language. They further provide a holistic evaluation across various scenarios, including preference steering and sentiment following. The effectiveness of the proposed method is demonstrated on several benchmark datasets.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Make Algorithm 1 clearer. Some parameters, such as t, are not defined.\n2. The figure 4 results are very hard to read. I would recommend changing it into a Table. There is also a lack of subfigure captions to distinguish between subdatasets.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)