Personalized Visual Content Generation in Conversational Systems
Authors: Xianquan Wang
Zhaocheng Du
Huibo Xu
Shukang Yin
Yupeng Han
Jieming Zhu
Kai Zhang
Qi Liu
Venue: NeurIPS 2025 poster ()
Keywords: Conversational Systems
Visual Content Generation
Primary area: applications

Abstract: With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of "one-size-fits-all." To break this limitation and boost user engagement, in this paper, we introduce PCG (**P**ersonalized Visual **C**ontent **G**eneration), a unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personalization and the fidelity of generated images. Specifically, an LLM-powered Inclinations Analyzer is adopted to capture user likes and dislikes from context to construct personalized prompts. Moreover, we design a dual-stage LoRA mechanism—Global LoRA for understanding task-specific visual style, and Local LoRA for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images. Extensive experiments on benchmark conversational datasets—including objective metrics and GPT-based evaluations—demonstrate that our framework outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for conversational scenarios like e-commerce and real-world recommendation.

Personalized Visual Content Generation in Conversational Systems
Xianquan Wang",,
Zhaocheng Du Jieming Zhu?.
Huibo Xu", Kai Zhang
Shukang Yin', Qi Liu'
Yupeng Han',
University of Science and Technology of China Huawei Noah's Ark Lab
{xqwang, xhbxhb, xjtupanda, yupenghan) wut..utcc.... edu. cn zhaochengguu@uuaeei. com jiemingzhu&ieee org (kkzhang08, qiliuql]Oustc. www.busttteeeu............e enu.cn
Abstract
With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of min-size-fits-al. To break this limitation and boost user engagement, in this paper, we introduce PCG (Personalized Visual Content Generation), unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personaliza- tion and the fidelity generated images. Specifially, an LLM-powered Inclinations Analyzer is adopted to capture user likes and dislikes from context to construct per- sonalized prompts. Moreover, we design a dual-stage LoRA mechanism Coobaa LoRA for understanding task-specific visual style, and Local LoRA for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images. Extensive experiments benchmark conversational datasets- including objective metrics and GPT-based evaluations demonstrate that our framework outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for conversational scenarios like e-commerce and real-world recommendation.
Introduction
The rise of large language models (LLMs) 34 35 has sparked conversational systems across industries. For instance, e-commerce platforms can recommend preferred items through multi-turn while music apps interactively suggest album cover (3). They operate by
conversations 30 5
dynamically analyzing users' inclinations (likes or dislikes) during interactions [14] when users show strong interest in specific item categories, or strongly advocate for specific item, the dialog prioritizes related content; conversely, explicit rejections reduce recommendations about those
items 12 2TI Usually, the systems also include the item image attract users, shown Figure
A picture is worth a thousand words; images are the most intuitive and vivid representation of items. Unfortunately, current conversational systems presented identical item representations to all users, even though different users prefer different points of the same item. For example, the
"Equal contribution
"Corresponding author: Kai Zhang [elzhangg88ustc.eddu..o
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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User liked items
Target items
How about these two movies?
TE PRRAET
How about The Pursuit of Happyness?
User
ADVENTURG
User
enjoy happy movies, but could it be with more family elements?
I'm looking for movie.
w/o
I AUGHTER
personalized
ChatBot
ChatBot
with personalized
Figure 1: A toy example of content generation personalization in movie conversational systems. In this example, the personalization focus on visual content.
parenting, adventure, and inspirational themes of movie The Pursuil of Happyness attract different users. Incorporating personalized image content into the system is expected to significantly enhance the system's 's level of personalization, which is expected lead to a more satisfying user experience, increase user engagement, and improve alignment between user intent and system sulptt-particulatt in application domains such entertainment [3], e-commerce
[8]. and digital assistants [37].
However, little to no work has systematically explored personalized visual content generation within conversational systems, and achieving this goal is far from easy. In the conversation process, generating the visual content that reflects the user's preferences on the target item faces three main challenges. First, how to extract the user's inclinations that be understood by content generator based on the conversation content. These inclinations consist of two parts: one is the positive preference, or what is known as "likes"; the other is the aversion certain styles, "dislikes." These inclinations may reflect on certain style types or may be deeply hidden in historical images. This requires thorough understanding 77) and analysis of the conversation history to accurately and effectively summarize the user's ' inclinations. Another challenge is how to incorporate the user's preferred visual elements into the image generation process, as even if a user enjoys "comedy" films, the preferred elements may vary greatly. The image references in conversational systems can provide beneficial guidance. Finally, how to preserve the identity in the generation process, ensuring that while personalization is reflected, the identity characteristics (such as the actor or semantic information) remain as consistent as possible during the inclinations' conditioning process.
To address the three challenges mentioned above, we constitutes one of the earliest comprehensive attempts to form a unified framework PCG (Personalized Visual Content Generation) in conver- sational systems. First, we utilize a language model to summarize and analyze past interaction items, extracting both positive and negative user preferences. In this process, the language model acts as analyzer, with word weight modeling to ensure that the output remains within expectations. These summaries form user preference descriptions, which guide the generation process include or exclude content. Second, integrating previously favored content elements into the generation is also challenging. Based on the contextual understanding ability of base models, we propose strategy that directly incorporates historical item images by concatenating each other on unified canvas. This allows the generation process be conditioned on visual preferences without modifying the model architecture. Finally, ensuring identity consistency requires the model to understand the relation between the target item and historical images. To facilitate this, we propose the sequence invariance strategy for historical items condition, and introduce global-local LoRA mechanism for guidance, which help the model better capture this relationship.
For evaluation, we adopt two commonly used benchmark datasets in conversational systems to validate the effectiveness of our method. In the objective experiment, we use various low-level and high-level metrics to assess the degree of personalization and fidelity of the generated outputs. In the GPT evaluation study, we further evaluate image aspects such as style and coherence. Experimental results demonstrate the effectiveness of our approach, and this work may open up new research direction at the intersection of conversational Al and visual generation. The code is publicly available at fihttps: //gi thub. com/xqqustc/POG

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2 Related Work
2.1 Conversational Systems
Conversational systems (20 have been widely explored fields such as robotics, smart shopping assistants, and recommender systems 126 33 28 With the advancement of language models (38 36
for text [17] and language processing, these systems have evolved from handling simple dialogues to understanding complex semantics 344 The rise of large language models has enabled deeper comprchension, allowing systems to better understand user needs and empathize with user emo-
tions [13]. This enhances the sense of engagement and satisfaction during interactions [39]. However, most current systems focus only on interaction at each turn, with little attention paid to personalizing rich visual content based on the conversations. Our work addresses this gap by introducing novel approach to content personalization within conversational systems.
2.2 Content Generation
With the rapid development of large language models and visual generation models, personalized content generation has drawn increasing attention [22 32). For instance, personalization large models enables the generation of responses tailored to users' specific needs and preferences. The primary goal of such personalization to enhance user satisfaction by producing outputs that better align with individual requirements and expectations. A key function of this type of personalization is to generate text content that matches the user's writing style [I These approaches have been applied in various contexts, such user-level personalization based on interaction history, role- level customization for specific personas, and global preference aligmment (e.g., cthics, factuality). However, these efforts have been predominantly limited to text-based personalization.
Personalization of visual content has also been widely explored. For example, several studies have used Diffusion-basee models c 24 4 or the FLUX architecture [27] to customize image generation. Their success built on the foundation of Diffusion Transformers (DiTs). These methods support prompt-driven generation and style transfer, among other features. Recently, large multimodal models 2 have integrated both understanding (16) and generation capabilities. However, they still lack the ability actively infer user preferences and rely heavily explicit user input. As highlighted in pioneering works [25] 29 33), some studies have attempted to explore personalization in the multimodal domain (e.g., images). Nevertheless, these works fail to model user interests from conversational history [8) leaving a blank personalizing visual content within multi-turn conversations.
3 Preliminaries
Let P be the set of participants and I the set of items. A conversation is represented as a sequence of interaction turns: C (Pr, St, 4 where at each turn t, participant pt P (either user or chatbot) produces an utterance empty no items are referenced. The conversation captures the alternating turns between the user and the agent, along with the evolving context of item mentions.
which may mention a subset of items i, C i. The set i, can be
The ultimate goal of this work is to enable personalized content generation for the target item recommended by chatbot in a multi-turn dialogue. Specifically, after interacting with a user across multiple conversational turns, the chatbot recommends target item i". Based the entire conversation history, the system aims to generate personalized content associated with i" that aligns with the user's preferences expressed during the conversation.
While the definition of "content" can be broad, in this paper, we focus on generating visual content, such as posters, thumbnails, or cover images. The generated visual should not only represent the semantics of the item ě", but also incorporate stylistic or thematic elements that resonate with the user's individual taste. Formally, the task be defined of. arg max.ev,- Score(v C).
Ideally, the target v/. should receive the highest scores across all metrics, such as similarity to conversation history or the user's preferences. For visual content, the score can refer to the semantic similarity between the generated image [29] and the original image, or between the image and items the user has previously liked [35].
3

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(1) Inclinations Analysis
Conversations History
Personalized Prompl
Item Aggregation
Incliaations
Original Torget hlem
items
Dallbce
Fie Doaal??
Plepp* 6:4)
"Cartee'
Faporicias
disecss seneng thene
and Bie sene redt staries.
Cartoon,
Global (Style) Local LolAAs
Hew about /octet Basy
Easy CosyConfool of Pipaline
Visual Condition
Figure 2: The PCG pipeline consists of two main components. On the left, the inclination analyzer summarizes the user's likes and dislikes. On the right, both global and local LoRAs control the style condition, the original target item condition, and the liked items condition. The personalized prompt has access to the other two tokens, while each the two tokens is only aware its own state.
Methods
In this section, introduce the role of each component in generating the final output. First, the analyzer summarizes the user's conversation inclinations (e.g.. likes and dislikes), providing a deep understanding of past inclinations. Then, better integrate previously preferred item content, leverage DiT'' contextual understanding capabilities. Moreover, by using the sequence invariance of historical interactions under shuffled order, the model improves its ability retrieve relevant interest-related content.
4.1 Inclinations Analyzer
During conversations, the user's inclinations primarily manifest in two ways: (1) explicit statements, such as want a xxx type of item" or "I don't 't like items with xxx style," and (2) reactions to the item candidates provided by the system, for instance, liking Titanic Flipped, which indirectly suggests preference for romantic items or items about love. These two types of preferences complement each other, and analyzing them in unified manner is essential for accurately understanding the user's inclinations (both likes and dislikes).
Let the user's positive inclination (preference) word set be:
W = (w1, W2 W.
(I)
Each word we is associated with non-negative preference score t, sww)) E Rzo, which reflects the user's level of interest in that item. To normalize the scores into a valid probability distribution (as shown in Figure 2), define the user's tendency distribution T as:
T {(w,, 4)) w. E W).
For example, in the movie poster scenario, the user's original preference scores could be:
T = {("Sci-fi", 0.375), ("Warm", .0.25), ("Mystery". 0.25), ("Comedy", 0.125))
The inclination analyzer converts users' explicit statements and implicit feedback into quantifiable preference weights through decay-aware weighting mechanism. Given a tendency distribution T = [[w, 4 compute the final recommendation weights
(w:) = 1 + t;.
(3)
This design provides two distinctive advantages over conventional normalization approaches: Non- Zero Preservation: By establishing a baseline weight of 1.0 for all attributes, our method prevents complete climination of low-probability preferences. Formally, have 7(,)) > 1.0, Vw; W.

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Therefore, even marginally preferred attributes (e.g., t, < 0.1) retain influence in the generation process. And for Relative Emphasis Modulation: The additive weighting compresses extreme differences while maintaining ordinal relationships. For any two attributes (wy, wx)):
+ 4j (ww)) 1 + tx
7(1z)
when i2 > ts-
(4)
tk
This property mitigates over-specializzaion in recommendations, especially in the process of genera- tion process. After obtaining each wi and its corresponding 7(;)), ) we aim to ensure the generation model effectively captures this relationship. Studies show that DiT-based models can leverage (word, weight) pairs for prompt weight adjustment [19]. Hence, we design a positive prompt template, denoted as Gpos. which can be formulated as:
9pos Thiis is a [task] for fitem_name], with styles and types of (wi 7((w)),....
where wy E W. In practice, we choose the top-K highest weight words to construct the personalized prompt. For the negative prompt Gugg. it mainly includes the types disliked by the user. This is used to reduce the appearance of elements that may cause discomfort. Since these cases are relatively rare, directly use the disliked types to form the prompt without weighting the negative words.
This prompt acts as a textual supervision signal, guiding the generation process during the distribution mapping phase. G (Spos, } forms the overall personalized prompt.
However, relying solely on textual supervisory signals introduces several issues, such the model's inability to capture the inherent semantic information of the original target item and integrate elements from previously liked item content. We additionally incorporate the content and textual information of both the target item and historically liked items for generation condition.
4.2 Visual Content Condition
To enable the model to condition on both the original item and historical liked items, is essential to incorporate historical information into the generation process. Specifically, the personalized prompt should guide the generation jointly with the visual content, while maintaining as much independence possible between the original item and historical items. This is because the generation process introduces personalized elements based on the original item. A straightforward approach is to use Group Diffusion Transformers (). However, such methods have two major drawbacks: they heavily influenced by the VAE and fail to make historical visual elements visible to each other during the condition generation process.
Inspired by [0], we propose a method to allow textual and visual modeling to jointly guide the generation process without modifying any model architecture. Specifically, historical visual content is concatenated before encoding and then fed into the encoder as a single image. This approach is motivated by [10), which demonstrated that DiT can effectively handle concatenated images and merged prompts, capturing contextual relationships within them.
4.2.1 Multi-Item Aggregation
Given item images (A) user liked in the conversation, we spatially concatenate them into unified canvas I
Tent = C(1 I2..,, E
where C(-) denotes row-wise concatenation operator. Similarly, we could also define column- operator. In general, for vertically oriented visual items, we can stack them row by row for concatena- tion, while for horizontally oriented items, we concatenate them along the column dimension. This approach ensures that the aspect ratio of resulting image does not vary significantly, enabling the text to better guide the image generation process. It is important to note that during the generation process, the fused information should not depend on the order of concatenation. Therefore, the training process should be designed to minimize sensitivity to the order of concatenation.
5

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4.2.2 Sequence Invariance
To make the model more robust to the order of input features while keeping their meaning unchanged, we propose a mechanism called Sequence Invariance. This method ensures that the model's output does not depend the order in which input items concatenated. We implement this by randomly changing the order of concatenated input images during training.
We define the concatenated image Teat block matrix in Equation 5 with J to Ix. Each block 1; E IR xX is an individual input image, and Lemt E I [x XXC represents all N images placed side by side. To apply Sequence Invariance, we use a permutation matrix / {0, 13 NxN to reorder the blocks in Tea:
Lat, shutfled = P.. Ieat:
(6)
where P satisfies:
1
if block j moves position i,
Ps 0 otherwise,
PT P PPT
In.
(7)
Here, P an orthogonal matrix, and IN the x identity matrix. This operation permutes the positions of J1, I2,..., IN without changing their content.
During training, new permutation matrix P is randomly sampled for each batch. P is chosen uniformly from the set: Py = {P E {0, 1J PPP- = IN This ensures all possible input orders are equally used in training and helps the model learn order-independent features.
We construct P from a random permutation x E Sy, the set of all orderings of N elements:
1 if T() =, Pi
{1 0 otherwise.
(8)
At inference time, we use the identity matrix P IN to keep the original input order:
Teat, shuffled = P. Iaa IN
Iea
Iear-
(9)
In Appendix C.2 we provide an in-depth analysis of the Sequence Invariance.
4.2.3 Global and Local LoRA
Based on concatenated images, how these images guide the generation process is key challenge. In typical text-to-image generation tasks, textual prompts alone often serve the condition. While this enables diverse outputs, it may also result in large deviations from the original visual content. common strategy in the community is to first train a task-specific LoRA, which, through fine-tuning, embeds useful prior knowledge into the model for better alignment with the task.
In our case, we aim to condition the generation not only on the original image but also on historical visual information. This motivates the introduction of two types of LoRA modules: global LoRA to control the overall style and semantics, and local LoRA that selectively influences the content branch to better fuse visual conditions. The global LoRA is obtained based on the target task, though integrating visual features into the local LoRA is non-trivial.
Thanks the powerful plugin system of EasyCentrol, strategy during generation. As shown in Figure _ built on FLUX, EasyControl provides additional LoRA weights for branches such subject and background, enabling the model to incorporate user- specific visual history. Meanwhile, the global LoRA is applied across noise, personalized prompts, and visual condition branches ensure consistency with the task. By jointly using task-specific global LoRA and visually-oriented local LoRA, our framework effectively balances personalized preferences with the faithful rendering of target item content during generation. Specifically, we adopt the same architecture as EasyControl, where the tokens from the personalized prompt attend to both the subject and background through local LoRA. In contrast, the subject and background tokens only interact with their own content. This design is important for our task because it ensures that control consistently guided by the prompt. It also keeps the blending of subject and background under textual supervision, preventing mutual interference.
fully leverage this dual-conditioning

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Global LoRA In this stage, the base model parameters 0 are frozen, and only the gltbal(style) LoRA parameters Puyle should be trained. Specifically, no visual or dialogue conditioning is used. Our model based on FLUUX. 2, which uses How-matching loss. Therefore, the same loss function is adopted as:
Salle (00 | (Ir, - (x1 - xo)1l²
(10)
where Zy denotes Gaussian noise, 31 denotes the original data sample, and d is intermediate state obtained by interpolating between them, typically defined as = t)zo + tz The style LoRA parameters Psyle could be optimized using the gradient Valgga Layle-
Training Local LoRA with Obtained Global LoRA After the style LoRA Ouyle is obtained and fixed, introduce two local LoRA modules used for conversation context, Psubj (weights are set from EasyControl, used in inference) and @bg. to enable personalized visual conditioning. Fe(C), encoding conversation history information including user inclinations and concatenated images, guides the generation process. The generation is conditioned on these cues and can be optimized via:
Ciocal Er, =00 =1. Fe(C) (C) ||uo dug (Ie, t, Fe(C)) (æ1 =00
(11)
where gradients are applied Ony (iee, Va, Lieal o 0), while thayle remains frozen. In our setting, the LoRA weights are also initialized using EasyControl and can be further fine-tuned with the loss defined above.
Inference During the inference process, it is necessary to balance the influence strength of the subject and background elements on the generated content. Let the weights of these two local LoRAs be represented by λ and λ2 respectively. Generally, Y1 is set to be properly larger than A since the background serves supplementary role. We generally set Å₁ and X2 0.85 by default, while careful tuning in the possible vicinity of these values may further improve performance.
Experiment
In this section, we introduce the base model, comparison models, and details of the datasets used in our experiments. Although many conversational systems exist in the field, high-quality conversation data with available visual content remains scarce. Following previous works of conversational recommender systems [23 40). we conduct experiments on two conversational recommendation datasets set in movie scenarios. The hyperparameters we adopted are shown Appendix G
5.1 Dataset
Dataset
Dialogs
Utterances
Avg. Turns
The two datasets are classic benchmarks for movie conversational recommender systems, containing many high-quality interactions with the systems.
E-ReDial
756
12,003
15.9
Inspired
1,001
35,811
10.73
Table 1: Data overview of conversational datasets in the movie scenario. sets with ratio of 8:1:1, The generated results were evaluated on the test set.
For both datasets, the original data was ran- domly split into training, validation, and test
5.2 Evaluation Metrics
Morcover, we use CLIP to measure similarity between images and texts (CLIP-T), as well as between images (CLIP-I), in order to evaluate the semantic and visual similarity between the generated and original images. The Deep Inception Score (DIS) Score is also defined to assess the cosine similarity of the last layer of two images after passing through the Inception model.
In our evaluation, different image similarity metrics are computed using various image resolutions. Specifically, Inception-bases metrics such as FID and DIS are processed at the resolution, while CLIP-based metrics (CLIP score and Image CLIP score) and LPIPS are evaluated at 224 x 224. For MS-SSIM, we also maintain the resolution for image pairs to capture fine-grained structural similarities. Moreover, for LPIPS calculation, we use AlexNet as the base model.

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Table 2: The objective evaluation results of E-ReDial.
Historical Liked
Target
Types
Models
FID
MS-SSIM
CLIP-TT
DIST
CLIP-TT
CLIP-11
MS-SSIM"
LPIPS!
SD3
0.1285
26.52
84.97
28.82
74.14
0.1286
0.6831
21.32
Text-tn-Image
FLUX. 1-dey
0,1741
25.02
82.56
27.49
68.14
0.1875
0.6692
26.51
TasyControl-SUBR
0.1437
25.97
87.94
30.72
77.92
0.2166
0.5561
20.20
Image-to-Imuge
EasyControl-B
0.1514
24.62
80.20
26.96
64.12
0.1765
0.6742
25.01
PCG (Ours)
0.1589
26.52
85.76
73.94
0.1938
0.6238
Table 3: The objective evaluation results of Inspired.
Historical Liked
Target
Types
Models
FIDJ
MS-SSIM
CLII--T1
DIS1
CLIP-T1
CLIP-11
MS-SSIM
LPIPSL
SD3
0,1145
26.12
84.24
27.81
69.31
0.1236
0.6810
20.72
Text-to-Image
FLUX.1 1-dey
0.1819
25.36
83.25
27.17
68.32
0.1767
0.6673
25.66
Easy CosyContoll-UBB
0.1521
25.87
88.52
30,70
79.08
0.2201
0.5371
19.74
Image-to-Imuge
CasyContol-BGG
0,1906
24.89
81.09
26.38
61.62
0.1746
0,6947
25.41
PCG (Ours)
0.1764
26.23
85.55
29.56
72.20
0.1872
0.6291
22.41
5.3
Main Experiment (Objective Evaluation)
The main experiment primarily involves the analysis of objective metrics related to the content. We focus on two key aspects: the integration of historical liked items into the generated content and the fidelity to the target item intended for interaction. Specifically, for EasyControl, we evaluate the generative performance in two separate settings: -SUBJ, where only information from the target item used for conditioning, and -BG, where only background information is used as the conditioning input. Both settings are based on EasyControl framework. The experiment on E-ReDial and Inspired can be found in Table 2 and
As shown in Table the conventional text-to-image methods struggle maintain high fidelity to the target item. Specifically, the similarity between the generated movie posters and the captions of the target item's 's posters, measured by CLIP-T, around 27-29, generally lower than that achieved by image-to seve-to-iman im-i models. This highlights the importance of effectively conditioning on the target item ensure model fidelity. On the other hand, eext-to-image models perform relatively well in integrating historical interests. For example, the FLUX.11dee model achieves high SSIM similarity scores, and SD3 outperforms in the CLIP-T metric when compared to historical liked items. For FasyControl-BCG it can be observed that solely using concatenated historical items does not improve historical relevance compared to FLUXX. -dev. This indicates that single LoRA for background signals cannot generate coherent guiding signal with the prompt, meaning it fails to maintain fidelity or integrate historical interests.
On E-ReDial, when using the target item directly as the image condition along with user historical items text prompts, the historical information can be partially integrated. This approach achieves an MS-SSIM of 0. 0.1437 against historical images, showing noticeable improvement compared to SD3. However, the integration is still not thorough enough, as evidenced by the two historical interest metrics where CasyControl-SUB. underperforms compared to our proposed PCG framework. This further validates the effectiveness of our method. On Inspired in Table 3 PCG excels at preserving historical user preferences. While the baseline FLUXX. 1 -dde can partially reflect user history, its semantic fidelity (measured by CLIP-T) is significantly lower than PCG's. EasyControl-SUDB shows strong performance in capturing details of the target item, but this comes at the cost of weaker integration of information from previously liked items. These results highlight PCG's ability to strike strong balance between fidelity and personalization.
Overall, text-to-image methods exhibit promising results in capturing historical interests, likely because explicitly incorporating user history into prompts enhances generative capability. In contrast, image-to-image methods demonstrate superior fidelity, possibly due stronger perceptual alignment with image signals. Achieving optimal performance in both aspects simultaneously is challenging, as there an inherent trade-off between them. Nevertheless, PCG effectively integrates historical information while maintaining high fidelity, proving its overall superiority.

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Table 4: GPT evaluation score (subset).
Table 5: GPT evaluation score (full test set).
Model
V
I
T
Model
T
D
FLUX. d-v
3.00
2.00
2.25
2.75
FLUX.1-dev
3.94
3.23
3.26
3.97
SD-3
4.50
4.50
4.75
4.50
SD-3
3,48
3.13
3.11
3,40
EC-SUBJ
2.00
1.25
1.50
2.25
EC-SUBJ
4.14
3.82
3.86
4.03
EC-BG
3.25
3,25
3.00
3.75
EC-BG
3.81
3.61
3.31
3,95
PCG
5.00
5.00
4.75
5.00
PCG
4.34
4.01
4.17
4.38
5.4
GPT Evaluation
Due to the subjective nature of personalized generation, results, aiming to make the assessment more objective and targeted. When GPT-based models are unavailable, the Gemini series models serve good alternatives. In this part of the experiment, first randomly select subset of generated dialogues from the test set and compare them with other generated results. The relevant prompts are provided Appendix [[] According to the scores from GPT on E-ReDial subset (shown in Table our proposed PCG performs well across all evaluation aspects, indicating a good overall balance between fidelity and personalization. This gap substantial, demonstrating the exceptional performance of PCG in specific contexts and scenarios.
GPT-4o to evaluate the generated
On the full E-ReDial test set, PCG shows smaller However, PCG still achieves the best performance across all four dimensions. Apart from PCG, EC-SUBJ closely follows in each dimension, showing strong consistency with the objective metries evaluation. For example, its performance in objective metrics is outstanding, and it also scores highly in subjective evaluation, particularly in coherence and details. EC-BG performs well in the Integration dimension, indicating that background as condition is effective. However. due to lack of good synergy with other techniques, it slightly underperforms compared to the base FLUX. 1-dev model in visual presentation metrics.
gap compared other base models.
5.5 Hyperparameter Analysis
We analyze impact of λ and Ag on the generated results. In our experiments, we fix a = 1.0 and vary A2, which controls the background strength. Overall, increasing A2 improves both fidelity and personalization. However, when X2 1, the per- formance drops significantly. Notably, the CLIP-T vs. target item score remains stable for X2 < 1, but shows a clear decline at X2 1.2. This is likely be- cause the background becomes too dominant, lead- ing visual clutter, or what refer chaotic "patterned images." Thus, selecting an appropriate A2 is crucial balance fidelity and personalization for different subjects. Further hyperparameter anal- ysis of it, and the impact of training steps on LoRA are provided in Appendix F.T
CLIP-T vs Target item and MS-SSIM vs Historical Liked
0.160
strgge Item)
0.155
1550 like
30
0.150
0.145
26
CLIP-T target item)
MS-SSIM (vs historical Rtmm)
0.130
0.4
0.6
0.8
1.0
12
The impact of d2 E-ReDial dataset.
Figure 3:
5.6 Visual Comparison
we present the generation results of five models based on four conversations. Two the original conversations shown in Appendix []] It can be observed that PCG achieves good balance between fidelity to the original image and personalization.
In Figure
For example, in the second row, with the case of Superbad, the user's favorite movies include typical teen comedies such as Meet the Parents and The Hangover. PCG preserves the high-school vibe and playful tone of Superbad, while enhancing the character positions and background colors in the

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Liked Items Target Item
Dev
SD-3
EC-SUB3
EC- BG
PCG
WATERBBO
CORE
SUPERA
Superbad
TOKPN
TAIKEN
Figure 4: Visual Comparison PCG with other models. Here EC represents EasyControl.
poster, making it more vibrant and youthful. In contrast, the outputs of other models show significant style changes simply replace the background without maintaining the original atmosphere. In the fourth row, the target item is the classic Taken poster, which features a strong cold tone, tense atmosphere, and the iconic composition of the protagonist standing alone. PCG retains the dark tone and tension of the original Taken poster while integrating the frontal portrait style of historical items, effectively capturing elements of the user's 's inclinations.
Overall, PCG generates results that naturally combine historical interests while maintaining high fidelity the target item. In comparison, EC-SUBJ performs similarly, but it is slightly less effective in capturing the overall style and transferring the atmosphere.
6 Conclusion
To address the lack of visual diversity in conversational systems, we propose PCG, novel personal- ized generation framework that integrates user preferences while maintaining fidelity to the original item. Specifically, PCG leverages the strong language understanding capabilities of large models to efficiently infer user likes and dislikes, which are then used to construct personalized prompts guiding the generation process. In addition, better incorporate visual elements that users may prefer, we introduce visual content conditioning method. This method uses both global and local LoRA As to align the generation with the target item and historically liked items. We evaluate PCG on standard benchmarks, focusing on both fidelity to the target item and personalization based on user history. Our results demonstrate that PCG show great generation ability, which may offer promising new direction for improving user experience in conversational systems.
Acknowledgment
This research was partially supported by the National Natural Science Foundation of China (Grants No. 62406303, 62525606), the Anhui Province Science and Technology Innovation Project (20242300002010),, and the USTC Bihe Youth Program for Interdisciplinary Innovation (BH- 202518).
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Appendix
A Limitations
Although our method demonstrates its effectiveness through subjective and objective evaluations, hyperparameter analysis, ablation experiments, and result visualizations, it has some limitations. The approach is highly dependent on the quality the original images (fortunately, the images used in this study of high resolution), the ability of the base LLM for interest analysis, and its relatively slow generation speed, which can be a challenge for real-time conversations.
We hope that the ideas presented in this paper will offer new insights for personalized content generation in conversational systems, pave the way for further developments, and inspire more excellent work. In our future work, we will also focus on refining the generation of human-relatee details and addressing the current issue where imperfect content fidelity may cause generated outputs to stray somewhat from the original intent.
B Broader Impact
The proposed visual content generation framework, PCG, demonstrates a significant advancement in personalized content synthesis by leveraging user preferences to create visually compelling and contextually aligned content, By integrating liked items with the target movie, PCG enhances visual consistency and thematic relevance, bridging the gap between user-specific taste and cinematic representation. This approach not only amplifies user engagement but also introduces novel ways for users to interact with media content, offering more immersive and personalized experience.
The main advantage of PCG lies in its ability to accurately capture thematic elements from both the target movie and the user's preferred visual styles. Unlike traditional generation methods that merely focus on stylistic transfer, PCG effectively balances semantic alignment with visual aesthetics, leading outputs that are not only artistically pleasing but also contextually meaningful. This capability opens up new opportunities in content marketing, fan engagement, and interactive media, allowing for dynamically generated visuals that resonate with individual tastes.
However, the method also introduces potential ethical considerations. Personalized content generation, if not regulated properly, may inadvertently promote biased representations based on user history, reinforeing stereotypes or unbalanced views. Furthermore, the use of copyrighted materials for personalized synthesis raises questions about intellectual property rights and fair use, particularly when generated content resembles original artworks. Addressing these challenges requires clear guidelines on data privacy, responsible use of AI in media creation, and proper attribution for derivative works to ensure ethical and fair application.
C Theoratical Support for PCG
C.1 Flow Matching Loss Formulation
C.1.1 Loss Analysis on Global LoRA
We provide the flow matching loss Global LoRA as:
Layle (o, | (Fr, t) (x) zo)]l²-
(12)
This loss function trains the LoRA parameters dsyye to predict the velocity field that transforms noise samples To (0, I) into data samples 21 Pdata- The intermediate state 2; is defined through linear interpolation:
(1 - t)æo + tri
(13)
The gradient with respect to the style LoRA parameters dayle is computed as:
V days Layle E, (00.1)
12 (x1 zo)) V duyse Uo+ (Ir,
(14)
21

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This gradient drives the parameter updates through gradient descent:
dosyye
V
Oaste fistyle.
(15)
dr
Under the LoRA parameterization sayle
BA, where E Raxr and E Rrxd the gradients
decompose
V Aastyle = B" V Crag Laylee
(16)
BBCyyle Voni Lay
(17)
This low-rank parameterization constrains updates to a compact subspace, enabling efficient and modular style adaptation while preserving the base model's capabilities.
C.1.2 Application to Local LoRAs
The gradient flow for Local LoRA analysis identical that of Style LoRA and will not be repeated here.
C.2 Sequence Invariance as Flow Matching Regularization
In our work, how sequence invariance affects the training or inference process remains open question. fact, the sequence invariance strategy can be interpreted as regularization method in the flow matching framework.
Proposition 1 (Sequence Invariance Regularization). Let ग E SN be any permulation of the input source sequence. Define the permuted source distribution as q8. The ideal goal is:
Lru(go q1) = Lru(go Vn E SN,
(18)
where CFM is the flow matching loss.
Introducing random permutation matrices P during training is equivalent to minimizing the expected flow matching cost:
Ex- [CrM 91)].
(19)
This encourages the model to learn representations that are invariant input orderings.
C.2.1 Group Theory Perspective on Sequence Invariance
From group theory standpoint, sequence invariance corresponds to invariance under the symmetric group Sx.
A function / is sequence-invarian if and only if:
f(P f(x), VP E Px.
(20)
This can be enforced using the Reynolds operator:
f(x)
Σ J(P, x)). NI TESH
1
(21)
This suggests that random permutations during training project the model toward the invariant function space.
C.3 The Choice of Aj and Ag-
Let A = 1 and X2 0.85 be the default fusion weights used during inference. Here we prove why we could set the X for Global LoRA and change A2. These settings can justified using Bayesian perspective and Information Bottleneck view.
22

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C.3.1 Bayesian Optimal Weight Allocation for Multimodal Information Fusion
To explain why we set X A2. analyze the theoretical rationale behind this choice from Bayesian perspective. Given subject data Dunij and background data Dog. the target is to estimate the posterior p(z:) Dog). By Bayes' rule:
Dsbli Dog) p( D(uub x2) [ Dog (x) pz))
(22)
Taking logarithms and it could be obtained:
log p Dunhj, Dog) x logp(æ) + log D(Dantj a:) + log p( Diglz))
(23)
Assuming these parameterized by neural networks, the optimal fusion weights are inversely proportional to the conditional entropy:
A1 x H Habsa))
du H(Dbgl)))
(24)
Since subject data usually provides more certain information, this supports the setting Å X2-
C.3.2 Information Bottleneck View of Fusion
To explain why fix Å1 1 and vary A2, we provide proof from the Information Bottleneck perspective, showing that the ratio between the two (rather than their specific values) has the greatest impact on the fusion data. From the information bottleneck principle, optimal fusion maximizes relevant information while minimizing redundancy:
max T(Z.Y) (X)
(25)
where represent the input data, Y represent the target output, and Z represent the learned repre- sentation. We define entropy-weighte trade-off parameter. Then the ratio of fusion weights satisfies:
A1 ~
I(Zuuby: Y) 1 - Bog
(26)
A2
[(Zogi Y) 1 Bsubj
D Pseudo Code
D.1 Training Pseudo Code
Algorithm 1 Training Process with Sequence Invariance
1: Input: N item images {4, ) historical content
2: Step 1: Spatially concatenate images to form Tear E (N
3: Step 2: Generate permutation matrix P via:
4: Sample ग ~ Sx & construct P where P.j = I[x(}==1 =
5: Apply shuffling: Teat, shuffled P. Ient
6: Step 3: Obtain Global LoRA @sayle" 3
7: Step 4: Fine-tune Local LoRA Ong:
8: Freeze @ayle
9: Optimize obg using local loss Ciocal with conversational (text/image) context
10: Step 5: Update parameters of LoRA using gradient descent
movie conversational system scenarios, to facilitate reproducibility and reduce training re- source requirements, global LoRA can be adopted from https://civitai.c ctt.a/i.oommmmm/m////oe/// com/mooels//7786111133 flux-lora-movis www.wooowiieeeooeeeeee poster-moden.
23

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D.2 Inference Pseudo Code
The inference phase involves several modules, including user inclinations analysis, integration of historical items, as well as flow matching transformation and decoding processes. The following pseudo code illustrates the main parts of these processes in detail.
Algorithm 2 Inference Process with Global and Local LoRA
Input: Dialogue history C, reference images {4rr) including {4ea}
2: Step 1: Load pretrained components:
3: Base model parameters (FLUX.1-dev)
4:
Global LoRA Oxyyle (frozen, pretrained)
5:
Local LoRAs: @subj thg (fine-tuned)
6:
Step 2: Set LoRA weights:
7:
A 1.0 Subject LoRA weight
8: Àg 0.85 // Default background LoRA weight (supplementary signals)
9: Step 3: Analyze inclinations:
10: G AnclinationsAAallzze(()]) // Extract text inclinations
11: Step 4: Initialize noise and timestep:
12: Sample xa ~N. I) // Start from Gaussian noise at t 0
13: Set timestep schedule [t, with to = O, tr = 1, t E IR
14: Step 5: Flow Matching inference loop:
15: for 0 7 - do
Combine LoRA parameters:
17: @comhined = @ayle U Ay Asubj U A2 Ong
Apply EasyControl framework:
19:
Inject personalized prompt G to both subject & background branches with {
Ensure subject/backkround tokens interact within own content
21:
Predict velocity field:
Ut thomieee (It, t, Fe(C)) I/t, R
23:
Update latent via Euler method:
At t.+1 -
25:
Ft, = Ft. + Al ut:
end for
27:
Step 6: Decode final image:
28:
Igenerated = Decoder((1) // 21 is the sample t =
29: Output: Generated image Igenerated
E
Contextual Understanding Through Concatenation
We suggest that concatenation allows the DiTs model to capture the semantic information of pre- viously liked items. Although [10] provides empirical evidence of its effectivvness, it lacks deep analysis of the underlying mechanism. Here, we explain how DiTs integrates semantic context through concatenation by analyzing how the transformer's attention mechanism interprets background information.
E.1
Context Modeling Ability of Transformer Attention Mechanism
DiT, which is based on the Transformer architecture, leverages the Self-Attention mechanism that naturally captures long-range dependencies. When input sequences, such as image features text tokens, are concatenated, each token can attend to every other token in the sequence through attention weights. This process enables the establishment of global semantic relationships. Specifically, the model computes Query vector for each token, which is compared to the Key vectors of all other tokens to calculate similarities. The resulting similarity scores are used to weight and aggregate the corresponding Value vectors. This allows the model to focus on key information within the sequence, such as object features or scene styles, and incorporate this information into the current task. Additionally, the multi-head attention mechanism decomposes the attention computation into

---

multiple subspaces, each focusing on different dimensions of context (e.g., color, spatial arrangement). These multi- multi-dimensional semantic insights are then integrated to generate coherent outputs.
E.2
Input Concatenation Strategy Activating the Model's Contextual Representation Ability
DiT activates the model's contextual representation ability by concatenating image or text tokens, providing explicit contextual cues. Unlike traditional methods that concatenate only attention tokens, DiT directly concatenates multiple images into single input. This approach preserves both spatial and semantic relationships between images in the feature space. It enables the model to understand the overall theme of the image set and maintain consistency in style and logic during generation. In the case of PCG's Personalized prompt design, the model extracts global semantic framework through text encoder and transforms into contextual constraints for image generation. Furthermore, pretraining allows the model link abstract concepts in text with image features. When multiple image prompts are provided, the model can use its pretraining knowledge to infer the semantic relationships between the images. As a result, pretraining DiT enables efficient malttiimage understanding through In-Context Learning-
F
More Experiments
E1
Hyperparameter Analysis
In this section, we conduct an additional hyperparameter analysis.
F1111
Impact of Local LoRA Training Steps on Generation Quality
Table 6: The influence of Local LoRA training steps on E-ReDial.
Historical Liked
Target
Steps
FID.
MS-SSIM
CLIP-Tt
DIS
CLIP-TY
CLIP-It
MS-SSIMt
LPIPS
100
0.1470
26.38
86.15
30.39
76.01
0.1768
0.6354
21.91
300
0.1568
26.64
85.94
30.37
74.76
0.1770
0.6448
22.89
500
0.1542
26.54
86.23
30.30
75.47
0.1773
0.6393
22.67
700
0.1511
26.68
86.03
30.48
74.71
0.1689
0.6445
22.86
The number of training steps for LoRA significantly affects the generation results. If too large, it may lead to overfitting, while if too small, it may fail to capture the fusion of background information, lacking relevant capabilities. Therefore, during training, we select the most suitable checkpoint based on the generation performance on the validation set, which is then used for generation on the test set.
From Table 6 it can be observed that as the number of training steps increases, the model's ability to fuse historical liked items initially increases and then decreases, but overall it shows an upward overall
trend. Comparing the MS-SSIM and CLIP-T scores relative historical items, improvement in history fusion capability compared to training for 100 steps. However, there is a noticeable decline in subject fidelity, shown by the decrease in DIS and CLIP-related metrics for the target item. This indicates that achieving a balance between personalization and fidelity requires selecting the most appropriate training steps based the validation set for different tasks.
E.1.2 Deeper Impact of Variations in d and Ag
In Section 5.5 the main text, have analyzed the impact of different values of A on quantitative metrics, and here we present its effect on generation quality. As shown in Figure [ the generated results vary significantly with changes in Ag. When A around 0.3. the generated image incorporates some information from the liked items, but it still primarily reflects elements related to the target item. As 2 increases to around 0.6-0.8, a balance is achieved between fidelity to the target and personalization. However, when X2 exceeds 1, the generated result is mainly combination of liked items, as the strong guidance from background information limits the reflection of the original target item.
25

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Liked Items Target Item
0
100
500
1000
2000
WICLIAN
PIMEN
XMEN
50HISTORES
50 SORRSOAESS
AT
Prycho
Papcis BLACE
The BLACE
Parkee
MAR BRat
TAERY gIOS
JAIERY Boos
JAERY BIRSS
AHRRY Baos
TRATSPERMERS
TRANSFSRITEES
TRAISFSRTERG
TRANS/ORIERS
Figure 5: Generation results with different training steps for Local LoRA.
F.2
Ablation Studies
In this chapter, we conduct various ablation studies components and strategies, such as Sequence Invariance, to demonstrate the effectiveness of each part.
F.2.1 The Impact of Sequence Invariance
In this experiment, we examine the impact using (w/) and not using (w/o) Sequence Invariance (SI) on quantitative metrics, under the setting of training for 1000 steps with Local LoRA. As shown T after applying SI, the generated images better integrate historical information, achieving higher scores MS-SSIN and CLIP-T. Additionally, SI improves fidelity, reflected in better
26

---

Liked Items Target Item
0.3
0.6
0.85
1.2
TWATERBOY
WWATERBOY
HWATERBOV
MAYE
MAST
SuPERBID
SUPERBAC
TAKEN
Figure 6: The generation results with different X2-
performance on metrics like SSIM. While these differences may not be immediately noticeable the generated results, the metries clearly demonstrate the effectiveness of SI.
Table 7: The influence of Sequence Invariance E-ReDial.
Historical Liked
Target
Method
FIDI
MS-SSIMT
CLIP-TI
DISt
CLIP-Tt
CLIP-If
MS-SSIMI
LPIPS).
1000 steps w/o SI
0.1464 0,1488
26.53 26.69
85.92
30.33
75.67
0.1670
0.6496
22.68
1000 steps w/ SI
85.92
30.34
75.52
0.1710
0.6487
22.49
F.2.2
The Impact of Main Components
In this section, focus the impact of the following three main components generation quality metrics: Global LoRA, Negative Inclinations and Score Reweighting. These components play crucial role in both the fidelity and personalization of the generated results.
Table 8: The ablation study of E-ReDial.
Historical Liked
Target
Method
FID|
MS-SSIMt
CLIP-Tt
DISt
CLIP-T1
CLIP-It
MS-SSIM
LPIPS.,
w/o Global LoRA
0.1521
26.45
87.29
29.11
71.31
0.1890
0.5993
21.13
w/o Negative Prompt
0.1601
26.47
85.63
30.55
73.89
0.1945
0.6257
21.98
w/o Score Reweight
0.1594
26.46
85.72
30.39
73.39
0.1970
0.6250
21.92
From the ablation study in Table 3 we find that Global LoRA is the most important component, playing an essential role in both personalization and fidelity. This is because generating movie posters requires strong communityypre-traineee trained LoRA to provide relevant guidance; without it, the model struggles to meaningfully connect content with the poster. Next, the Score Reweighting mechanism significantly impacts the fidelity of the generated results. Without Score Reweighting, the generated outputs, compared to target items, show a decrease quality, and the CL.IPTT score relative to historical liked items also drops. This indicates that reweighting helps generate higher-quality content.
27

---

Lastly, the impact of Negative Prompt is minimal, likely because other components compensate for the negative influence on generation.
In terms of visualizing the generated results, we can clearly observe that Global LoRA has the most signifieant impact the generation quality, while the effects of Negative Prompt and Score Reweighting are less apparent visually, despite the differences shown in the evaluation metrics.
w/o Global Lora
w/o NP
w/o SR
PCG
Liked Items
Target Item
SOFRST BOTTES
A.
RIDRIN
WORT
WORTI
Bikey Broes
TANERY Bligs
HabRRy Blos
Biicy grips
THAIS SAITERS
TRAnssyomment
TRANSFORMERS
TRANSFORMERS
Figure 7: Ablation studies, Here NP represents Negative Prompt, and SR represents Score Reweight- ing.
28

---

G Implementation Details
As mentioned in Section we use Qwen3-8B as the LLM generate user inclinations and GPT-4o for evaluation. Here, we provide detailed description of the configuration used for Qwen model. We generate outputs with the following parameters: maximum 128 new tokens, sampling enabled with temperature of 0.7, top-p sampling with probability of 0.8, top-k sampling with a limit of 20, and minimum probability of 0.0.
When fine-tuning PCG LoRA based on EasyControl, we strictly follow its recommended settings. 10 (based on the FLUX. I-dev pre-trained model, using
The overall learning rate single A100-80G GPU with a batch size of 1). The two types of LoRAs in the two-stage training share the same learning rate.
The optimizer used is AdamW W with the parameters:
B1 = 0.9, B2 = 0.999,
weight decay = 1 x 10
The dimension of the low-rank matrices is 128.
H Inclinations Analyzer Prompt
Bole:
You are an expert. Please extract the user 's movie- related common styles, colors, types words and their probabilities from the following dialogue. Output format: a list, each element is [ descriptive word, probabilityl, probability is float between and Note that the descriptive word should NOT be the movie names. For example, a user liked movie Forrest Gump, the output should be like this: [['encourage', 0.8], [' ['comddy', 0.3]].
Dialogue content: {dialogue_text}
Role:
You are an expert. Please extract the user's disliked movie- related common styles, colors, types words and their probabilities from the following dialogue. Output format: Some words that the dislikes, each word is string. If the any movie, please return an empty list. For example, user disliked movie Saving Private Ryan, the output may be like this: war, horror.
doean dislike
Dialogue content: (dialogue_text)
Listing I: Few-shot prompt template for inclinations analysis.
I GPT-4o Evaluation Prompt
Role:
I first provide you some historical liked images and the target item image. Then I will provide you with set of movie posters. Please evaluate them based on the following four dimensions, with each dimension being rated on scale of fair in your analysis and provide clear reasoning for each rating. Finally, calculate the total score and rank the posters accordingly. The evaluation dimensions are as follovs:
Be objective and
1. Visual Aesthetics Are the color combinations harmonious and visually impactful?
Is the composition balanced and layered?
Does have the quality and feel of a cinematic movie poster?
http:///ugggnggaaaecooo///e//e/
29

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Integration Level Are the elements from the Target Item and History Liked Items naturally integrated? Like the colors, elements and roles of History Liked Items.
Does appear seamless and unified, if they belong the universe?
3 Thematic Coherence
Does successfully convey the main storyline of the Target Item while highlighting the characteristics of the Liked Items?
Is the background story immediately understandable to the viewer?
Detail Representation
Are background, characters, lighting, and text details well- refined?
Are there any obvious signs of poor blending noticeable seams?
Scoring Criteria:
1: Very Poor Completely uncoordinated
2: Poor Noticeable flaws
3: Fair Basic integration but lacks refinement
4: Good Visually consistent and well detailed
5: Excellent Perfectly blended with cinematic quality
Listing 2: Prompt for GPT Evaluation.
J Original Conversations
{
"assistant": "Hi", "user": "Hello! How are you? Could you recommend some comedy ss? I had tough day. "
{
"assistant"; "I recommend [Meet the Parents (2000) since it funny comedy and it make you laugh. It tells the story of Greg, future son son-in-lat and male nurse, who meets strict Atther-in-lav..
},
{
"user": "No, haven't."
{
"assistant"; "You have to see it. If you're interested in more family comedies, also try [Meet the Fockers (2004)] the hilarious sequel. Another great option is [The Hangover (2009)), where three buddies wake up from a bachelor party in Las Vegas with no memory and the groom missing."
{
"user":
check out."
{
"user": "Oh, [The Hangover (2009)] funny"
30

---

b,
{
recommend [Superbad (2007)], since it's hilarious and funny. It's about three high schoolers trying to bring alcohol to a party to impress their peers. I'm sure after watching, you can't 't stop laughing."
"assistant";
}.
"user": "Have not seen that either. Will have to check it out."
}.
{
"user": "Thank you for the reconmendations"
},
{
"assistant"; "You re welcome, take care
}
]
Listing 3: Conversation for Row-3
"user": "Hey!" !" (
" "assistant"; "Hi!"
"user": "I in need of some good action movies, not superhero movies though [The Fast and the Furious] movies. love movies like [Speed (1994)] with Keanu Reeves, or [The Taking Pelham 123 (2009)] with Joh Travolta"
"user": "Any suggestions?"
{
"assistant": "Since you like action movies, then I think this movie will suit your taste: [Looper (2012)). The sci-fi is not foolproof as the director would like it to be, but the film is appealing and entertaining enough with a nice blend of action and thrills. I believe that you will enjoy yourself while watching it }
"user": "I have with Bruce Willis! That one is such great movie 1"
}
"assistant"; "If you're looking for great action movies, recommend [John Wick (2014)]. It's about first-clas killer who is forced to go back to work to get revenge after his retirement. It of Keanu best action movies, you should really try it!"
}
{
"user": "I've heard of it and know the sequel came out, but have never seen it"
{
"user": "What is that about?"
}
"assistant": "As you have watched [Looper (2012)), then you can try [John Wick (2014)). It's one of Keanu 's best! It was directed by Chad Stahelski, David Leitch. Other actors include Michael
31

---

Nyqvist, Alfie Allen and Willem Dafoe. I believe this movie will give you unprecedented experience. It's similar storyline [Taken (2008)]. A man seeking vengeance for his loved one. It's really good."
"user": "That sounds really good!" "
}.
{
"user": "I think the story line is really good thank you"
}.
{
"assistant"; "You 're welcome!"
}.
{
"assistant"; "I hope you check it out! Enjoy your day!"
]
Listing 4: Conversation for Row-4
32


Reviews:
[{"review_id": "1yIead1Wkt", "reviewer": "Reviewer_kNWa", "summary": "This paper introduces PiViC (Personalized Visual Content), a framework designed for generating personalized visual content in conversational systems. The authors propose two-stage LoRA, which combines Global LoRA for task-specific styles and Local LoRA for user-preferred visual elements, enabling the generation of images that reflect user preferences.", "questions": "1. I would like to suggest the authors performing broader evaluations on more benchmarks other than the movie conversational benchmarks, e.g., e-commerce product posters.\n\n2. Considering this is an application technique, he authors should consider discussing computational complexities and wall-clock time measures more carefully.\n\n3. I wonder the visual effects of adjusting the LoRA hyper-parameters. Provide more detailed analysis would be interesting.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Stengths:\n\n1. The paper is easy to follow. The figure illustrations are clear.\n2. The proposed method is simple and extendable, and has potential industrial values.\n3. The paper employs both objective metrics (e.g., FID, MS-SSIM) and subjective GPT-based evaluations to provide more thorough evaluation results.\n\n- Weaknesses:\n\n1. The evaluations are only performed on two movie conversational recommender benchmarks, which is relatively limited. Expanding the evaluation to a wider variety of domains (e.g., non-entertainment or non-image-based recommendations) would strengthen its generalizability.   \n2. The LoRAs and integration of multiple visual elements may increase computational cost, which could impact scalability, especially for real-time applications.\n3. Ablation studies and analysis are limited.", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your recognition of our work, and we are very happy to answer your questions : )\n\n**W1 & Q1.**  \nOur evaluation is conducted on movie datasets because our work targets *conversational recommender systems*, and so far, almost all conversational recommender systems are **built on two movie datasets** without using other datasets [1-5].  \n[1] Parameter-Efficient Conversational Recommender System as a Language Processing Task, EACL'24  \n[2] Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts, Arxiv'25  \n[3] Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning, KDD'22  \n[4] COLA: Improving conversational recommender systems by collaborative augmentation, AAAI'23  \n[5] A Large Language Model Enhanced Conversational Recommender System, Arxiv'23  \n\nThrough a more detailed investigation, we found that other *niche* conversational recommender system datasets **do not** contain history and target item images, which creates a large gap from the PiViC setting. We hope you can understand this difference. Nevertheless, we have additionally included results on the E-Redial dataset, using *closed-source models* (GPT-4o, Gemini 2.5-Pro) for objective metric comparison, for your reference (in W2 & Q2).\n\n---\n\n**W2 & Q2.**  \nUsing LoRAs indeed increases inference latency to some extent. However, considering real-time applications, we compared our method with mature commercial products (e.g., GPT-4o and Gemini 2.5-Pro):\n\n| Model   | Time per Image (s) |\n|---------|--------------------|\n| GPT-4o  | ~40s               |\n| Gemini  | ~25s               |\n| PiViC   | ~20s               |\n\nAs shown, our runtime is within an acceptable range compared to the other two. In fact, we have already initiated new work to optimize the generation process via **KV cache**, since the same target item images are often used multiple times as keys and values during conditioning. Please stay tuned for further improvements!\n\nAdditionally, we include the comparison of generation quality with these closed-source products:\n\n| Model          | History                                     |       |        |         |        | Target         |         | FID↓   |\n|----------------|----------------------------------------------|-------|--------|---------|--------|----------------|---------|--------|\n|                | CLIP-T↑     | CLIP-I↑ | LPIPS↓ | MS-SSIM↑ | DIS↑   | CLIP-T↑       | MS-SSIM↑ |        |\n| GPT-4o         | 25.18       | 57.80  | 0.6857 | **0.1800** | 0.8289 | **30.63**    | **0.2158** | 23.28  |\n| Gemini 2.5-Pro | 25.06       | 58.98  | 0.6860 | 0.1426     | 0.8229 | 28.86         | 0.1511   | 22.12  |\n| PiViC (Ours)   | **26.52**   | **59.12** | **0.6714** | 0.1589 | **0.8336** | 30.52         | 0.1938   | **21.88** |\n\nWe can see that PiViC achieves a better balance between personalization and fidelity. GPT-4o generates highly faithful images, but still lags behind PiViC in personalization.\n\n---\n\n**W3.**  \nThank you for your suggestion. Our ablation study is placed in the *Supplementary Material.zip* (Section J.2, to be moved into the main paper in the final version), which might be easy to overlook. The ablation study covers four aspects: **Sequence Invariance**, **Global LoRA**, **Negative Prompt**, and **Score Reweight**, along with objective metric comparisons. For the latter three, we also provide intuitive visual comparisons of the generated results.\n\n---\n\n**Q3.**  \nThis experiment is indeed very interesting and highly valuable for understanding the internal mechanisms of LoRA. Unfortunately, due to this year's conference guidance, which **strictly forbids** authors from updating images in the repository, we cannot include them at this stage. However, we will add a discussion on this part in the final version. \n\nThank you very much for your in-depth suggestions!"}, {"review_id": "fHKPY5KrKL", "reviewer": "Reviewer_Xfff", "summary": "This paper introduces PiViC, a novel framework for personalized visual content generation in conversational systems, which leverages user inclinations extracted via LLMs and integrates them through a dual-stage LoRA mechanism (global and local) to adaptively guide image synthesis. Extensive experiments on benchmark datasets show that PiViC effectively balances fidelity to target items and personalization from user history, outperforming strong baselines like FLUX and EasyControl.", "questions": "1. Have you considered interactive or real-time updates to the personalized visual generation if a user changes preferences mid-dialog?\n2. In your framework, how scalable is the dual-stage LoRA approach when conditioning on a large number of historical items?", "limitations": "1. The paper primarily benchmarks against tailored diffusion-based pipelines and does not include direct comparisons with recent large multimodal models (e.g., GPT-4o or Gemini) that can jointly handle long conversational histories and multi-image conditioning, which might offer strong baselines.\n2. While PiViC effectively captures user inclinations through dialogue, it heavily relies on conversation-derived preferences and does not explore integrating richer user behavior signals (such as clicks or ratings), potentially limiting its personalization depth in broader applications.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### strengths\n1. The paper is well-written and clearly structured, making easy to understand.\n2. The paper demonstrates a thoughtful integration of language understanding and visual generation, enabling a seamless blend of user preferences with item-specific content.\n\n---\n\n### weaknesses\n1. Lack of comparisons with alternative baselines, such as directly employing LVLMs for inclination analysis.\n2. The experimental comparisons are not comprehensive; to my knowledge, models like GPT-4o and Gemini can process multiple images and long contexts. It would be valuable to see how such models perform on the E-ReDial and Inspired datasets.", "was_revised": true, "final_justification": "The experimental results provided by the authors during the rebuttal stage have largely addressed my concerns, so I have increased my score accordingly.", "rebuttal": "We sincerely thank you for reviewing our paper and for your positive recognition of its clarity and novelty. We are pleased to address your questions and hope this will persuade you to **raise your rating** for our work~~\n\n**W1.**  \nYour suggestion is excellent. In the initial design, we did attempt to use VLMs as an analyzer to obtain users’ inclinations (by feeding both the dialogue and the mentioned movie poster images into the model) to achieve more stable user modeling. However, this approach required significantly more resources and inference time compared to PiViC, making it impractical.  \nNevertheless, we have supplemented our work with a comparison experiment using another open-source generative model (**Kandinsky**) on the E-Redial dataset as an alternative baseline:\n\n| Model          | History                                     |       |        |         |        | Target         |         | FID↓   |\n|----------------|----------------------------------------------|-------|--------|---------|--------|----------------|---------|--------|\n|                | CLIP-T↑     | CLIP-I↑ | LPIPS↓ | MS-SSIM↑ | DIS↑   | CLIP-T↑       | MS-SSIM↑ |        |\n| Kandinsky      | **26.93**   | 56.71    | 0.6940 | 0.0924     | 0.8120 | 29.99         | **0.3884** | 25.24  |\n| PiViC (Ours)   | 26.52       | **59.12** | **0.6714** | **0.1589** | **0.8336** | **30.52** | 0.1938   | **21.88** |\n\nThis further demonstrates PiViC’s effectiveness.\n\n---\n\n**W2 & L1.**  \nYour proposed experiment is indeed *very nice*, and we are happy to accept it.  \nSince GPT-4o and Gemini cannot generate images via API, we used the official GPT-4o platform and the Gemini 2.5 Pro subscription version to generate images. As you know, this **collection process is highly time-consuming and labor-intensive**, so we conducted the experiment on the E-Redial dataset.  \nThe prompt used for both GPT and Gemini was:\n```\nGenerate a movie poster of xxx based on the user interest shown in the below dialogue: \\n <dialogue>\n```\n\nDue to platform restrictions, some images could not be generated (likely because of prohibited content), with reminders such as:\n\n> I can't generate that image because the request violates our content policies.  \n> Please feel free to share a different idea or request, and I'd be happy to help!\n\nThis resulted in *11 test set images* for GPT-4o being excluded from evaluation. As closed-source models, both have been fully trained on target items, so we focus on their ability to integrate historical interests.  \nDue to this year’s **rebuttal policy**, we cannot share generated images, but we believe the objective metrics below can **largely address your concerns**:\n\n| Model          | History                                     |       |        |         |        | Target         |         | FID↓   |\n|----------------|----------------------------------------------|-------|--------|---------|--------|----------------|---------|--------|\n|                | CLIP-T↑     | CLIP-I↑ | LPIPS↓ | MS-SSIM↑ | DIS↑   | CLIP-T↑       | MS-SSIM↑ |        |\n| GPT-4o         | 25.18       | 57.80    | 0.6857 | **0.1800**     | 0.8289 | **30.63** | **0.2158** | 23.28  |\n| Gemini 2.5-Pro | 25.06       | 58.98   | 0.6860  | 0.1426         | 0.8229 | 28.86     | 0.1511   | 22.12  |\n| PiViC (Ours)   | **26.52**   | **59.12** | **0.6714** | 0.1589     | **0.8336** | 30.52     | 0.1938   | **21.88** |\n\nWe can see that while GPT-4o produces highly faithful posters, its ability to incorporate historical information is inferior to our proposed PiViC. This suggests these models have limited capacity for understanding and integrating conversational information.  \nGemini’s fidelity and personalization are weaker than GPT-4o’s, a finding consistent with reports from some existing benchmark sites. These results further **highlight PiViC’s strength in reflecting user interests in conversation**.\n\n---\n\n**Q1.**  \nThis is an excellent question. Although we have not observed this phenomenon in our existing datasets, it is a valuable point.  \nFor such cases, an *inclinations register* can be maintained to store users’ long-term interests. When a shift occurs, both the stored inclinations and new ones can be analyzed by the analyzer, reducing the analysis length and allowing quick decoding of updated inclinations.  \nMoreover, since LoRAs learn the ability to integrate historical liked items, they can still adapt when new inclinations appear.\n\n---\n\n**Q2.**  \nScalability involves two aspects:  \n(1) Analyzing inclinations in long dialogues, and  \n(2) Handling a huge number of historical items.  \n\nFor long dialogues, our Q1 solution applies.  \nFor the dual-stage LoRA (handling historical & target images), the target image is always a single one, and regardless of the number of historical images, we concatenate them before conditioning. This extra cost lies only in the concatenation step (no extra computational overhead) and is negligible. Therefore, PiViC is scalable.\n\n---\n\n**L2.**  \nWe share your concern. We tried to find a conversational dataset with explicit feedback (e.g., ratings or clicks), but due to dataset limitations, we only obtained dialogue-based signals.  \nIf you could point us to a conversational recommender dataset with direct supervision signals like clicks/ratings, it would indeed highlight the merit of our PiViC. We also plan to explore this direction and will incorporate click/rating signals in future versions. In fact, click/rating data could directly score inclinations, serving as a highly reliable reflection of user interests.\n\nSincere thanks for your suggestion! : )"}, {"review_id": "7OBCDFvQnn", "reviewer": "Reviewer_3nXA", "summary": "This paper explores the tasks and new methods of personalized generation in conversational systems. This paper attempts to solve the problem that the content generated by conversational systems is not personalized enough, and proposes PiViC: using a large model to extract user inclinations, and using a flux architecture with multi-loras to incorporate the user's historical interests, so that the generated content is more in line with the user's preferences. Comprehensive experiments prove the effectiveness of the method.", "questions": "1. Did the author try more datasets? Although you mentioned that the current conversational recommendation system mainly uses these two movie datasets, is this a common practice or best practice? It would be better if there were data from more fields.\n2. The article did not provide a more detailed interpretation of the case study of the generated results, for example, it could explain how the generated results better reflect the user's conversational interests.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The motivation of the article is reasonable. For dialogue systems, previous work rarely considers content personalization and adaptation. This is an interesting idea.\n2. The framework is innovative and solves the gap between user dialogue history content and visual content generation, so that the dialogue system can generate & display content that users like.\n3. The experiment is detailed. The experiment covers modules such as subjective, objective, GPT analysis, ablation studies, and hyperparameter analysis, proving that the proposed PiViC framework can better reflect user historical interests.\n4. The technology used is theoretically analyzed, which highlights the rationality of the Inclinations Analyzer and Visual Content Condition related modules the paper used.\n\nWeaknesses:\n1. Although the method is solid, the article mainly relies on the existing architecture. If the architecture can be designed to be more unified (for example, personalized generation can be performed without two stages), the contribution will be more highlighted.\n2. The article did not conduct a human study. Does using GPT for scoring mean that the generated content is more in line with the user's interests and preferences?\n3. Some major experiments (such as ablation studies) should be placed in the main text to ensure that the content is self-consistent.", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your high recognition of our work — it motivates us to continue improving. We are happy to address your questions.  \n\n---\n\n**W1.**  \nThis is indeed the direction we aim to explore next. However, as you can see, many current unified multimodal understanding–generation models (e.g., **Janus** [1]) still decouple *multimodal understanding* and *image generation* (even if they reside within the same model). We expect and believe that future designs will build upon our work with more integrated approaches.  \n\n[1] Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation  \n\n---\n\n**W2.**  \nWe have attempted such experiments, but we found that participants in the human study often scored images according to their pre-existing personal preferences, which could bias the evaluation.  \nTherefore, we ultimately chose to use **objective metrics** and **GPT-based evaluation** to assess generation quality.  \n\n---\n\n**W3.**  \nThank you for the suggestion. Other reviewers also mentioned this point. In the final version, we will include more important experiments — including the ablation study — directly in the main text.  \n\n---\n\n**Q1.**  \nOur evaluation focuses on movie datasets because our work is designed for **conversational systems**, and to date, almost all such systems are **built on two movie datasets** [1–5], without adoption of other domains.  \n\n[1] *Parameter-Efficient Conversational Recommender System as a Language Processing Task*, EACL’24  \n[2] *Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts*, Arxiv’25  \n[3] *Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning*, KDD’22  \n[4] *COLA: Improving Conversational Recommender Systems by Collaborative Augmentation*, AAAI’23  \n[5] *A Large Language Model Enhanced Conversational Recommender System*, Arxiv’23  \n\nAfter a more careful investigation, we found that other niche conversational recommender datasets **do not** provide both *historical* and *target* item images, which makes them fundamentally incompatible with PiViC’s setting. We hope this clarifies our choice.  \n\n---\n\n**Q2.**  \nWe have provided more direct analyses of generated results in the **Supplementary Material.zip** (Appendix). Please refer to it for details. In the final version, we will also consider adding more intuitive qualitative comparisons of generated results."}, {"review_id": "XTV7v6NFJZ", "reviewer": "Reviewer_Q8gb", "summary": "This paper introduces PiViC generation, a unified framework for personalizing item images within conversational systems, addressing the \"one-size-fits-all\" dilemma in such systems. It leverages the strong language understanding capabilities of large models to efficiently infer user preferences, which are then used to construct personalized prompts that guide the image generation process.", "questions": "I am particularly concerned about Weakness 1, as it has a direct impact on the practical application of this approach.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength：\n1.  The authors propose a novel task of integrating generative models into recommendation systems, which is a highly innovative idea.\n2.  The manuscript is clearly written and easy to understand.\n\n\nWeaknesses：\n1. I find your idea of combining diffusion models with the user’s preference to generate personalized content very interesting. However, I am curious about the practical application of this approach. In recommendation systems, the recommended items actually exist, whereas the content generated by diffusion models may not correspond to real items. For example, as shown in Figure 1, a movie poster can be generated based on a user's preferences, but the corresponding movie may not exist in reality. How do you envision this technology being applied in practice, given that the generated content might not have a real-world counterpart?\n2. I think that both EasyControl-SUBJ and EasyControl-BG are components of PiViC. Therefore, it would be more appropriate to include a comparison with image-to-image methods that are not based on EasyControl.\n3. I think it is difficult to observe the superiority of the proposed method in Figure 3. Besides, conducting a comprehensive ablation study is crucial because it reveals the effectiveness of each individual component.\n4. In terms of methodology, I think the authors first leverage LLMs to construct text prompts, and then refer to EasyControl during the diffusion generation process. Therefore, the methodological novelty is somewhat limited.", "was_revised": true, "final_justification": "I find the idea interesting, and my concerns have been addressed. Therefore, I am inclined to give a positive rating.", "rebuttal": "We sincerely thank you for the overall positive evaluation and rating of our work !!! It is our honor to discuss our research with you~~ \n\n---\n\n**W1.**  \nYour concern directly aligns with one of our optimization goals — the **fidelity** of generated content. If the generated image deviates too far from the original target item, the meaning of personalization diminishes.  \nAs shown in our results (e.g., *Fig. 1* — the first row “Waterboy” and the last row “Taken”), the generated outputs match the target items well, and objective metrics also confirm PiViC’s strong fidelity performance.  \n\nThat said, due to the **inherent limitations of current generative models**, achieving complete fidelity remains challenging. We would like to emphasize that PiViC is a **complete personalization pipeline** — if the base model itself achieves perfect fidelity, our method can also deliver fully faithful personalization on top of it. Since fidelity is a fundamental challenge in image generation, we believe this is an area where *we, other researchers, and the broader community* must work together.  \n\n---\n\n**W2.**  \nWe have added an additional **image-to-image** baseline (Kandinsky) for reference:  \n\n| Model          | History                                     |       |        |         |        | Target         |         | FID↓   |\n|----------------|----------------------------------------------|-------|--------|---------|--------|----------------|---------|--------|\n|                | CLIP-T↑     | CLIP-I↑ | LPIPS↓ | MS-SSIM↑ | DIS↑   | CLIP-T↑       | MS-SSIM↑ |        |\n| Kandinsky         | **26.93**       | 56.71    | 0.6940 | 0.0924     | 0.8120 | 29.99      | **0.3884**   | 25.24  |\n| PiViC (Ours)   | 26.52   | **59.12** | **0.6714** | **0.1589**   | **0.8336** | **30.52**         | 0.1938   | **21.88** |\n\nPiViC achieves **superior performance** in both personalization and fidelity.  \n\nWe also provide comparisons with **closed-source products** (GPT‑4o, Gemini 2.5‑Pro):  \n\n| Model          | History                                     |       |        |         |        | Target         |         | FID↓   |\n|----------------|----------------------------------------------|-------|--------|---------|--------|----------------|---------|--------|\n|                | CLIP-T↑     | CLIP-I↑ | LPIPS↓ | MS-SSIM↑ | DIS↑   | CLIP-T↑       | MS-SSIM↑ |        |\n| GPT-4o         | 25.18       | 57.80    | 0.6857 | **0.1800**     | 0.8289 | **30.63**      | **0.2158**   | 23.28  |\n| Gemini 2.5-Pro | 25.06       | 58.98   | 0.6860  | 0.1426|        0.8229 | 28.86         | 0.1511 | 22.12  |\n| PiViC (Ours)   | **26.52**   | **59.12** | **0.6714** | 0.1589   | **0.8336** | 30.52         | 0.1938   | **21.88** |\n\nPiViC offers a **better trade-off between personalization and fidelity**, with a clear improvement in integrating *historical liked items*.  \n\n---\n**W3.**  \nIndeed, as shown in our examples, **Superbad** (third row) integrates more background elements from the user’s historical styles (in softer tones), **Taken** (last row) combines the protagonist’s professional and composed demeanor with the forward-facing pose found in historical liked items, and **Waterboy** (first row) incorporates distinctive background motifs from historical inclinations. These examples illustrate PiViC’s ability to not only preserve fidelity to the target item but also seamlessly embed personalized elements drawn from historical interests.\n\n---\n**W4.**  \nThank you for the suggestion. As an **application-driven** work, our primary contribution is proposing a **novel pipeline** to address the problem of monotonous content presentation in conversational systems.  \nOur goal is to present the pipeline as a **holistic solution** rather than focusing solely on model-architecture-level innovations. Given that current generative systems are heavily dependent on pretrained base models, purely architectural changes may not be accessible or impactful for all researchers.  \n\nFrom a pragmatic perspective, we aim for PiViC to serve as a **starting point** for personalized visual generation in conversational systems — inspiring more **innovative, competitive, and groundbreaking** work built upon it, and contributing fresh ideas to the community."}]

Author rebuttal:

Meta-review: This paper introduces PiViC, a personalized visual content generation framework designed to personalize item images for conversational recommender systems. The authors develop an LLM-powered Inclinations Analyzer to analyze the user-system conversation history and identify users' likes and dislikes, generating personalized pictures based on this information. Global and local LoRA mechanisms are also proposed to learn historical context and maintain original image fidelity. The experiments are conducted on two movie recommender datasets, whose results are described in Tables 2 and 2.

Strengths:
+ The proposed method is simple, extensible, and has potential real-world applications
+ Thorough evaluations are conducted using both objective metrics and subjective GPT-based evaluations
+ The work is theoretically analyzed with the rational choice of the inclinations analyzer and visual content

Weaknesses:
- Using GPT as a judge can introduce bias in evaluating visual content, as shown in https://psrdataset.github.io/.

Overall, I agree with the reviewers' feedback and the authors' thoughtful rebuttal and support to see the paper accepted.
Decision: Accept (poster)