• 1.  The Oracle and The Prism: A Decoupled and Efficient Framework for Generative Recommendation Explanation
• 2.  Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
• 3.  An Efficient LLM-based Evolutional Recommendation with Locate-Forget-Update Paradigm
• 4.  Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob
• 5.  ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
• 6.  Multi-Aspect Cross-modal Quantization for Generative Recommendation
• 7.  SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs
• 8.  Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm
• 9.  DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home
• 10.  LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation
• 11.  WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
• 12.  CafeMed: Causal Attention Fusion Enhanced Medication Recommendation
• 13.  Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
• 14.  Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation
• 15.  Can We Predict the Next Question? A Collaborative Filtering Approach to Modeling User Behavior
• 16.  Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction

1.  The Oracle and The Prism: A Decoupled and Efficient Framework for Generative Recommendation Explanation

Jiaheng Zhang, Daqiang Zhang

https://arxiv.org/abs/2511.16543

The integration of Large Language Models (LLMs) into explainable recommendation systems often leads to a performance-efficiency trade-off in end-to-end architectures, where joint optimization of ranking and explanation can result in suboptimal compromises. To resolve this, we propose Prism, a novel decoupled framework that rigorously separates the recommendation process into a dedicated ranking stage and an explanation generation stage. Inspired by knowledge distillation, Prism leverages a powerful teacher LLM (e.g., FLAN-T5-XXL) as an Oracle to produce highfidelity explanatory knowledge.

A compact, fine-tuned student model (e.g., BART-Base), the Prism, then specializes in synthesizing this knowledge into personalized explanations. This decomposition ensures that each component is optimized for its specific objective, eliminating inherent conflicts in coupled models. Extensive experiments on benchmark datasets demonstrate that our 140M-parameter Prism model significantly outperforms its 11B-parameter teacher in human evaluations of faithfulness and personalization, while achieving a 24× speedup and a 10× reduction in memory consumption during inference. These results validate that decoupling, coupled with targeted distillation, provides an efficient and effective pathway to high-quality explainable recommendation.

2.  Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation

Elena V. Epure, Yashar Deldjoo, Bruno Sguerra, Markus Schedl, Manuel Moussallam

https://arxiv.org/abs/2511.16478

Music Recommender Systems (MRS) have long relied on an information-retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effective, this reductionist paradigm struggles to address the deeper question of what makes a good recommendation, and attempts to broaden evaluation, through user studies or fairness analyses, have had limited impact. The emergence of Large Language Models (LLMs) disrupts this framework: LLMs are generative rather than ranking-based, making standard accuracy metrics questionable. They also introduce challenges such as hallucinations, knowledge cutoffs, non-determinism, and opaque training data, rendering traditional train/test protocols difficult to interpret. At the same time, LLMs create new opportunities, enabling natural-language interaction and even allowing models to act as evaluators.

This work argues that the shift toward LLM-driven MRS requires rethinking evaluation. We first review how LLMs reshape user modeling, item modeling, and natural-language recommendation in music. We then examine evaluation practices from NLP, highlighting methodologies and open challenges relevant to MRS. Finally, we synthesize insights-focusing on how LLM prompting applies to MRS, to outline a structured set of success and risk dimensions. Our goal is to provide the MRS community with an updated, pedagogical, and cross-disciplinary perspective on evaluation.

3.  An Efficient LLM-based Evolutional Recommendation with Locate-Forget-Update Paradigm

Hao Liu, Le Wu, Min Hou, Han Wu, Kun Zhang, Xin Li, Si Wei

https://arxiv.org/abs/2511.16414

Nowadays, Large Language Models (LLMs) have shown exceptional performance in sequential recommendations, and the adoption of LLM-based recommender systems (LLMRec) is becoming increasingly widespread in existing e-commerce platforms. Despite the impressive performance, the constant high volume of new user-item interactions makes it difficult to adapt to the evolution of user preference over time, especially for LLM-based recommender systems. The challenge arises from the large number of parameters in LLMs, which makes traditional evolution methods (i.e., Re-training or Fine-tuning) impractical. Specifically, Re-training with all interactions results in prohibitively high computational costs. On the other hand, fine-tuning with only new interactions leads to preference forgetting among inactive users, ultimately compromising overall performance.

To tackle this problem, we propose EvoRec, an efficient Locate-Forget-Update framework designed for LLM-based recommender systems to model the evolution of user preferences. EvoRec identifies a small set of parameters associated with preference changes and updates them precisely, thereby saving computational resources while maintaining strong recommendation performance. Notably, the modified parameters account for only 30% of LoRA adapter parameters, with no additional parameters introduced. Extensive experiments on two real-world datasets demonstrate that, compared to existing methods, EvoRec not only efficiently evolves LLMRec to adapt to the preferences of active users, but also preserves the interests of inactive users from being disturbed during evolution. Codes are available at https://github.com/Tam-JQK/EvoRec

4.  Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob

Yun Lu, Xiaoyu Shi, Hong Xie, Chongjun Xia, Zhenhui Gong, Mingsheng Shang

https://arxiv.org/abs/2511.16248

This paper revisits fairness-aware interactive recommendation (e.g., TikTok, KuaiShou) by introducing a novel control knob, i.e., the lifecycle of items. We make threefold contributions. First, we conduct a comprehensive empirical analysis and uncover that item lifecycles in short-video platforms follow a compressed three-phase pattern, i.e., rapid growth, transient stability, and sharp decay, which significantly deviates from the classical four-stage model (introduction, growth, maturity, decline). Second, we introduce LHRL, a lifecycle-aware hierarchical reinforcement learning framework that dynamically harmonizes fairness and accuracy by leveraging phase-specific exposure dynamics.

LHRL consists of two key components: (1) PhaseFormer, a lightweight encoder combining STL decomposition and attention mechanisms for robust phase detection; (2) a two-level HRL agent, where the high-level policy imposes phase-aware fairness constraints, and the low-level policy optimizes immediate user engagement. This decoupled optimization allows for effective reconciliation between long-term equity and short-term utility. Third, experiments on multiple real-world interactive recommendation datasets demonstrate that LHRL significantly improves both fairness and user engagement. Furthermore, the integration of lifecycle-aware rewards into existing RL-based models consistently yields performance gains, highlighting the generalizability and practical value of our approach. https://github.com/luyunstar/LHRL

5.  ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

Sunwoo Kim, Geon Lee, Kyungho Kim, Jaemin Yoo, Kijung Shin

https://arxiv.org/abs/2511.15141

Recently, large language models (LLMs) have been widely used as recommender systems, owing to their strong reasoning capability and their effectiveness in handling cold-start items. To better adapt LLMs for recommendation, retrieval-augmented generation (RAG) has been incorporated. Most existing RAG methods are user-based, retrieving purchase patterns of users similar to the target user and providing them to the LLM. In this work, we propose ItemRAG, an item-based RAG method for LLM-based recommendation that retrieves relevant items (rather than users) from item-item co-purchase histories. ItemRAG helps LLMs capture co-purchase patterns among items, which are beneficial for recommendations. Especially, our retrieval strategy incorporates semantically similar items to better handle cold-start items and uses co-purchase frequencies to improve the relevance of the retrieved items. Through extensive experiments, we demonstrate that ItemRAG consistently (1) improves the zero-shot LLM-based recommender by up to 43% in Hit-Ratio-1 and (2) outperforms user-based RAG baselines under both standard and cold-start item recommendation settings.

6.  Multi-Aspect Cross-modal Quantization for Generative Recommendation

Fuwei Zhang, Xiaoyu Liu, Dongbo Xi, Jishen Yin, Huan Chen, Peng Yan, Fuzhen Zhuang, Zhao Zhang

https://arxiv.org/abs/2511.15122

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users' historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item by employing next-token prediction methods. The challenges of GR lie in constructing high-quality semantic identifiers (IDs) that are hierarchically organized, minimally conflicting, and conducive to effective generative model training. However, current approaches remain limited in their ability to harness multimodal information and to capture the deep and intricate interactions among diverse modalities, both of which are essential for learning high-quality semantic IDs and for effectively training GR models.

To address this, we propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec), which introduces multimodal information and incorporates it into both semantic ID learning and generative model training from different aspects. Specifically, we first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates and thus improves codebook usability through the complementary integration of multimodal information. In addition, to further enhance the generative ability of our GR model, we incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments. Finally, we conduct extensive experiments on three well-known recommendation datasets to demonstrate the effectiveness of our proposed method. https://github.com/zhangfw123/MACRec

7.  SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs

Bi Xue, Hong Wu, Lei Chen et al

https://arxiv.org/abs/2511.14881

Serving deep learning based recommendation models (DLRM) at scale is challenging. Existing systems rely on CPU-based ANN indexing and filtering services, suffering from non-negligible costs and forgoing joint optimization opportunities. Such inefficiency makes them difficult to support more complex model architectures, such as learned similarities and multi-task retrieval. In this paper, we propose SilverTorch, a model-based system for serving recommendation models on GPUs. SilverTorch unifies model serving by replacing standalone indexing and filtering services with layers of served models. We propose a Bloom index algorithm on GPUs for feature filtering and a tensor-native fused Int8 ANN kernel on GPUs for nearest neighbor search.

We further co-design the ANN search index and filtering index to reduce GPU memory utilization and eliminate unnecessary computation. Benefit from SilverTorch’s serving paradigm, we introduce a OverArch scoring layer and a Value Model to aggregate results across multitasks. These advancements improve the accuracy for retrieval and enable future studies for serving more complex models. For ranking, SilverTorch’s design accelerates item embedding calculation by caching the pre-calculated embeddings inside the serving model. Our evaluation on the industry-scale datasets show that SilverTorch achieves up to 5.6× lower latency and 23.7× higher throughput compared to the state-of-the-art approaches. We also demonstrate that SilverTorch’s solution is 13.35× more cost-efficient than CPU-based solution while improving accuracy via serving more complex models. SilverTorch serves over hundreds of models online across major products and recommends contents for billions of daily active users.

8.  Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm

Li Cuihong, Huang Xiaowen, Yin Chuanhuan, Sang Jitao

https://arxiv.org/abs/2511.14763

Membership Inference Attack (MIA) aims to determine if a data sample is used in the training dataset of a target model. Traditional MIA obtains feature of target model via shadow models and uses the feature to train attack model, but the scale and complexity of training or fine-tuning data for large language model (LLM)-based recommendation systems make shadow models difficult to construct. Knowledge distillation as a method for extracting knowledge contributes to construct a stronger reference model.

Knowledge distillation enables separate distillation for member and non-member data during the distillation process, enhancing the model's discriminative capability between the two in MIA. This paper propose a knowledge distillation-based MIA paradigm to improve the performance of membership inference attacks on LLM-based recommendation systems. Our paradigm introduces knowledge distillation to obtain a reference model, which enhances the reference model's ability to distinguish between member and non-member data. We obtain individual features from the reference model and train our attack model with fused feature. Our paradigm improves the attack performance of MIA compared to shadow model-based attack.

9.  DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home

Yuxiang Wang, Siwen Wang, Haowei Han, Ao Wang, Boya Liu, Yong Zhao, Chengbo Wu, Bin Zhu, Bin Qin, Xiaokai Zhou, Xiao Yan, Jiawei Jiang, Bo Du

https://arxiv.org/abs/2511.14227

Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment information, and device status. This task is crucial for enhancing user satisfaction and corporate profits. Existing recommendation models struggle with complex operation logic, diverse user preferences, and sensitive to suboptimal suggestions, limiting their applicability to IoT device operations. To address these issues, we propose DevPiolt, a LLM-based recommendation model for IoT device operations. Specifically, we first equip the LLM with fundamental domain knowledge of IoT operations via continual pre-training and multi-task fine-tuning. Then, we employ direct preference optimization to align the fine-tuned LLM with specific user preferences.

Finally, we design a confidence-based exposure control mechanism to avoid negative user experiences from low-quality recommendations. Extensive experiments show that DevPiolt significantly outperforms baselines on all datasets, with an average improvement of 69.5% across all metrics. DevPiolt has been practically deployed in Xiaomi Home app for one quarter, providing daily operation recommendations to 255,000 users. Online experiment results indicate a 21.6% increase in unique visitor device coverage and a 29.1% increase in page view acceptance rates.

10.  LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation

Hao Jiang, Guoquan Wang, Donglin Zhou, Sheng Yu, Yang Zeng, Wencong Zeng, Kun Gai, Guorui Zhou

https://arxiv.org/abs/2511.14221

Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-based methods typically encode item textual information via prompt design and generate discrete semantic IDs through item tokenization. However, in domain-specific tasks such as local-life services, simply injecting location information into prompts fails to capture fine-grained spatial characteristics and real-world distance awareness among items. To address this, we propose LGSID, an LLM-Aligned Geographic Item Tokenization Framework for Local-life Recommendation. This framework consists of two key components: (1) RL-based Geographic LLM Alignment, and (2) Hierarchical Geographic Item Tokenization. In the RL-based alignment module, we initially train a list-wise reward model to capture real-world spatial relationships among items.

We then introduce a novel G-DPO algorithm that uses pre-trained reward model to inject generalized spatial knowledge and collaborative signals into LLMs while preserving their semantic understanding. Furthermore, we propose a hierarchical geographic item tokenization strategy, where primary tokens are derived from discrete spatial and content attributes, and residual tokens are refined using the aligned LLM's geographic representation vectors. Extensive experiments on real-world Kuaishou industry datasets show that LGSID consistently outperforms state-of-the-art discriminative and generative recommendation models. Ablation studies, visualizations, and case studies further validate its effectiveness. https://github.com/JiangHaoPG11/LGSID

11.  WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web

Zihuai Zhao, Yujuan Ding, Wenqi Fan, Qing Li

https://arxiv.org/abs/2511.14182

Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender systems have emerged as a prevalent paradigm for advancing personalized recommendations. Recently, retrieval-augmented generation (RAG) has drawn growing interest to facilitate the recommendation capability of LLMs, incorporating useful information retrieved from external knowledge bases. However, as a rich source of up-to-date information, the web remains under-explored by existing RAG-based recommendations.

In particular, unique challenges are posed from two perspectives: one is to generate effective queries for web retrieval, considering the inherent knowledge gap between web search and recommendations; another challenge lies in harnessing online websites that contain substantial noisy content. To tackle these limitations, we propose WebRec, a novel web-based RAG framework, which takes advantage of the reasoning capability of LLMs to interpret recommendation tasks into queries of user preferences that cater to web retrieval. Moreover, given noisy web-retrieved information, where relevant pieces of evidence are scattered far apart, an insightful MP-Head is designed to enhance LLM attentions between distant tokens of relevant information via message passing. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed web-based RAG methods in recommendation scenarios.

12.  CafeMed: Causal Attention Fusion Enhanced Medication Recommendation

Kelin Ren, Chan-Yang Ju, Dong-Ho Lee

https://arxiv.org/abs/2511.14064

Medication recommendation systems play a crucial role in assisting clinicians with personalized treatment decisions. While existing approaches have made significant progress in learning medication representations, they suffer from two fundamental limitations: (i) treating medical entities as independent features without modeling their synergistic effects on medication selection; (ii) employing static causal relationships that fail to adapt to patient-specific contexts and health states. To address these challenges, we propose CafeMed, a framework that integrates dynamic causal reasoning with cross-modal attention for safe and accurate medication recommendation.

CafeMed introduces two key components: the Causal Weight Generator (CWG) that transforms static causal effects into dynamic modulation weights based on individual patient states, and the Channel Harmonized Attention Refinement Module (CHARM) that captures complex interdependencies between diagnoses and procedures. This design enables CafeMed to model how different medical conditions jointly influence treatment decisions while maintaining medication safety constraints. Extensive experiments on MIMIC-III and MIMIC-IV datasets demonstrate that CafeMed significantly outperforms state-of-the-art baselines, achieving superior accuracy in medication prediction while maintaining the lower drug--drug interaction rates. Our results indicate that incorporating dynamic causal relationships and cross-modal synergies leads to more clinically-aligned and personalized medication recommendations. Our code is released publicly at https://github.com/rkl71/CafeMed

13.  Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning

Miaomiao Cai, Min Hou, Lei Chen, Le Wu, Haoyue Bai, Yong Li, Meng Wang

https://arxiv.org/abs/2511.13041

Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This phenomenon makes CF-based methods tend to prioritize recommending popular items and performing unsatisfactorily on inactive users. Existing works address this issue by rebalancing training samples, reranking recommendation results, or making the modeling process robust to the bias. Despite their effectiveness, these approaches can compromise accuracy or be sensitive to weighting strategies, making them challenging to train.

In this paper, we deeply analyze the causes and effects of the biases and propose a framework to alleviate biases in recommendation from the perspective of representation distribution, namely Group-Alignment and Global-Uniformity Enhanced Representation Learning for Debiasing Recommendation (AURL). Specifically, we identify two significant problems in the representation distribution of users and items, namely group-discrepancy and global-collapse. These two problems directly lead to biases in the recommendation results. To this end, we propose two simple but effective regularizers in the representation space, respectively named group-alignment and global-uniformity. The goal of group-alignment is to bring the representation distribution of long-tail entities closer to that of popular entities, while global-uniformity aims to preserve the information of entities as much as possible by evenly distributing representations. Our method directly optimizes both the group-alignment and global-uniformity regularization terms to mitigate recommendation biases. Extensive experiments on three real datasets and various recommendation backbones verify the superiority of our proposed framework.

14.  Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation

Yu Hou, Won-Yong Shin

https://arxiv.org/abs/2511.12922

Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributions and semantics across item domains make it difficult to construct a unified tokenization that preserves domain-specific information. To address these challenges, we propose UniTok, a Unified item Tokenization framework that integrates our own mixture-of-experts (MoE) architecture with a series of codebooks to convert items into discrete tokens, enabling scalable tokenization while preserving semantic information across multiple item domains.

Specifically, items from different domains are first projected into a unified latent space through a shared encoder. They are then routed to domain-specific experts to capture the unique semantics, while a shared expert, which is always active, encodes common knowledge transferable across domains. Additionally, to mitigate semantic imbalance across domains, we present a mutual information calibration mechanism, which guides the model towards retaining similar levels of semantic information for each domain. Comprehensive experiments on wide-ranging real-world datasets demonstrate that the proposed UniTok framework is (a) highly effective: achieving up to 51.89% improvements over strong benchmarks, (b) theoretically sound: showing the analytical validity of our architectural design and optimization; and (c) highly generalizable: demonstrating robust performance across diverse domains without requiring per-domain retraining, a capability not supported by existing baselines. https://github.com/jackfrost168/UniTok

15.  Can We Predict the Next Question? A Collaborative Filtering Approach to Modeling User Behavior

Bokang Fu, Jiahao Wang, Xiaojing Liu, Yuli Liu

https://arxiv.org/abs/2511.12949

In recent years, large language models (LLMs) have excelled in language understanding and generation, powering advanced dialogue and recommendation systems. However, a significant limitation persists: these systems often model user preferences statically, failing to capture the dynamic and sequential nature of interactive behaviors. The sequence of a user's historical questions provides a rich, implicit signal of evolving interests and cognitive patterns, yet leveraging this temporal data for predictive tasks remains challenging due to the inherent disconnect between language modeling and behavioral sequence modeling.

To bridge this gap, we propose a Collaborative Filtering-enhanced Question Prediction (CFQP) framework. CFQP dynamically models evolving user-question interactions by integrating personalized memory modules with graph-based preference propagation. This dual mechanism allows the system to adaptively learn from user-specific histories while refining predictions through collaborative signals from similar users. Experimental results demonstrate that our approach effectively generates agents that mimic real-user questioning patterns, highlighting its potential for building proactive and adaptive dialogue systems.

16.  Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction

Moyu Zhang, Yujun Jin, Yun Chen, Jinxin Hu, Yu Zhang, Xiaoyi Zeng

https://arxiv.org/abs/2511.14403

Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely on a simple binary classification objective. However, existing generative models typically confine the generative paradigm to the training phase, primarily for representation learning. During online inference, they revert to a standard discriminative paradigm, failing to leverage their powerful generative capabilities to further improve prediction accuracy.

This fundamental asymmetry between the training and inference phases prevents the generative paradigm from realizing its full potential. To address this limitation, we propose the Symmetric Masked Generative Paradigm for CTR prediction (SGCTR), a novel framework that establishes symmetry between the training and inference phases. Specifically, after acquiring generative capabilities by learning feature dependencies during training, SGCTR applies the generative capabilities during online inference to iteratively redefine the features of input samples, which mitigates the impact of noisy features and enhances prediction accuracy. Extensive experiments validate the superiority of SGCTR, demonstrating that applying the generative paradigm symmetrically across both training and inference significantly unlocks its power in CTR prediction.

由于公众号试行乱序推送，您可能不再准时收到机器学习与推荐算法的推送。为了第一时间收到本号的干货内容， 请将本号设为星标，以及常点文末右下角的“在看”。