Recommender Systems

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32 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation
Oct 8, 2026Leikun Liang, Guoshuai Wang, Xingsheng He +4Recommender Systems Large Language Model-Based Recommendation
Alibaba Group Hangzhou, China
Personalization Matters: Long-Horizon Conversation Agent with User-Centric Information in Online Shopping Interactions
Oct 8, 2026Rena Gao, Yue Dai, Hao Guan +4Agentic Commerce Recommender Systems
ByteDance · The University of Melbourne · University of Technology Sydney +1
Multimodal Graph Retrieval-Augmented Sequential Recommendation via Collaborative Filtering Paths
Oct 8, 2026Jason Marcell Setiadi, Xin Cao, Lina YaoRecommender Systems Sequential Recommendation
University of New South Wales, Sydney, Australia
Reinforcement Learning with Conformal Action Sets: An Application to Sequential Recommendation
Oct 6, 2026Wenwen Si, Honghao WeiSequential Recommendation Reinforcement Learning
University of Pennsylvania · Washington State University
Personal-Agent Mediated Recommendation with Cross-Platform User History
Oct 6, 2026Yu Xia, Jiangfan Zhang, Jun Xiao +2LLM Personalization Recommender Systems
University of California San Diego · Meta · Meta AI
Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs
Oct 5, 2026Manousos Linardakis, Georgios AlexandridisRecommender Systems User Preference Modeling
School of Electrical & Computer Engineering National Technical University of Athens Zografou, 15780, Greece · Department of Digital Industry Technologies National & Kapodistrian University of Athens Evripos Complex, Psachna, 34400, Greece
Cut Binary Cross Entropy: Efficient Large-Vocabulary Loss and Gradient Kernels for Sequential Recommendation
Oct 4, 2026Yaoyiran Li, Haowen Ning, Mohamed HammadSequential Recommendation Recommender Systems
Google Cloud London, United Kingdom · Google Cloud Mountain View, USA
Ranking Bandits for Carousel Interfaces with Observable Browsing Depth
Oct 4, 2026Takuma Yasuda, Atsuyoshi NakamuraThompson Sampling Multi-Armed Bandits
Graduate School of Information Science and Technology Hokkaido University Sapporo, Japan
Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
Oct 1, 2026Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan +3Representation Learning Recommender Systems
KDM Lab, Dhirubhai Ambani University · Dhirubhai Ambani University · LCS2 Lab, Indian Institute of Technology Delhi
RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
Sep 30, 2026Xinwei Yang, Kelong Mao, Yudong Guo +4LLM Agent Evaluation AI Agent Benchmarks
College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China · Institute of Data Science, National University of Singapore
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
Sep 29, 2026Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal +8Multi-Armed Bandits Cold-Start Recommendation
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern · RPTU, Kaiserslautern-Landau · LMU, Munich
ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents
Sep 29, 2026Haohao Qu, Yongcheng Jing, Chun Hin Chan +3Long-Context Modeling Recommender Systems
The Hong Kong Polytechnic University Hong Kong · Nanyang Technological University Singapore
Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions
Sep 29, 2026Fanyu Zhu, Jiahui An, Ni JiExploration-Exploitation Tradeoff RL Exploration
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Institute for Brain Research, Beijing, Beijing Key Laboratory of Brain Science and Brain-Machine Interface · State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models
Sep 29, 2026Song-Li Wu, Xianquan Wang, Zhaocheng Du +2Diffusion Guidance Algorithmic Fairness
Tsinghua University · University of Science and Technology of China · Huawei Noah’s Ark Lab
Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Sep 28, 2026Ghazal Fazelnia, Paul Gigioli, Eliza Klyce +16Recommender Systems Large Language Model-Based Recommendation
Spotify, USA, UK, Sweden
Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems
Sep 28, 2026Cong Wang, Shoujin Wang, Yishuo Li +3Recommender Systems Diffusion Models
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, Shandong, China · University of Technology Sydney, Sydney, NSW, Australia · Department of Computer Science and Technology, Tongji University, Shanghai, China +1
Beyond One Epoch: Uncertainty-Weighted Sensitivity Regularization for Recommendation Models
Sep 28, 2026Richard Lettich, Shagun GuptaRecommender Systems
Meta Platforms, Inc. Menlo Park, CA, USA
Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems
Sep 24, 2026Shuang Yang, Zijie Zhuang, Changxin Lao +20AI Agents for Scientific Discovery Long-Horizon Agent Evaluation
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sep 24, 2026Sam Urmian, Qinyi Liu, Mohammad KhalilLearning to Rank Differential Privacy
University of Bergen Centre for the Science of Learning & Technology (SLATE) · City University of Macau School of Education
Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X
Sep 24, 2026Pan Li, Shuang GaoSocial Media Analysis Recommender Systems
Arizona State University
Learned Cross-Task Relationships in Multi-Task Models
Sep 23, 2026Victor Zhang, Yiping Yuan, Florian Raudies +4Multi-Task Learning Recommender Systems
Google San Bruno, CA, USA
Learning Collective Dynamics with Differentiable Gaussian Representations
Sep 23, 2026Jianxiang Ma, Mingfu Zhang, Xiaocui Yang +3Dynamical Systems Time Series Forecasting
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · OranAI, Shenzhen 518057, China · OranAI Ltd., City of Industry, CA 91748, USA
The Like Trap: Multi-Stage Poisoning against Agents in Similarity-based Recommendation Systems
Sep 22, 2026Yue Xing, Pengfei He, Zitao LiAI Agent Security Recommender Systems
Michigan State University
A Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators
Sep 22, 2026Chaehyun Kim, Sein Kim, Hongseok Kang +1Recommender Systems User Simulation
KAIST Daejeon, Republic of Korea
Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
Sep 21, 2026Sanjay Surendranath Girija, Aniruddh Nath, Li Wei +6Learning to Rank Multi-Task Learning
Google LLC Mountain View, CA, USA
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
Sep 17, 2026Noah Mamié, Laurin van den BerghRecommender Systems Large Language Model-Based Recommendation
University of Zurich Department of Informatics
Re2A: Situated Conversational Recommendation via Rubric-based Preference Reasoning and Alignment
Sep 16, 2026Dongding Lin, Jian Wang, Xiaoyan Zhao +1Recommender Systems Preference Alignment
Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University · The Chinese University of Hong Kong
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
Sep 16, 2026Venkat Srinivas, Chenzhang He, Sam Woodmansee +63Learning to Rank Recommender Systems
Scaling Articulated Rationales for MLLM-based Recommendation
Sep 15, 2026Haoke Xiao, Yueyang Liu, Yuhui Zhang +17Learning to Rank Recommender Systems
Kuaishou Technology
AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
Sep 15, 2026SungGeun Kim, Abhinav Narain, Daniel NemirovskySoftware Engineering Agents Recommender Systems
The Walt Disney Company, San Francisco, CA, USA

Oct 8, 2026, cs.CLJ/K move · Enter open · S save

Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation

Leikun Liang, Guoshuai Wang, Xingsheng He, Yushan Han+3

Alibaba Group Hangzhou, China

Recommender Systems Large Language Model-Based Recommendation Explainable Recommendation Sequential Recommendation Memory-Augmented Language Models

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Despite the adoption of large language models (LLMs) in recommendation systems, prevailing approaches mostly model single-type behaviors (e.g., views or purchases). Even when incorporating multiple behaviors, existing methods flatten heterogeneous actions into homogeneous token sequences, ignoring their distinct decision-making roles. This flattening fails to capture semantic hierarchies and contextual nuances in complex decision-making, such as trade-offs between price and quality. Consequently, performance degrades in critical ``difficult-choice'' scenarios involving highly similar items. To bridge this gap, we propose MARI (Memory-Augmented Recommendation with Interpretability), which grounds predictions in explicit, structured decision evidence. MARI maintains a Decision Memory Bank (DMB) that archives users' past rationales as Structured Decision Memories (SDMs): concise records of goals, constraints, and trade-offs. These SDMs are generated offline via Post-Hoc Decision Distillation from heterogeneous behaviors and user-generated content. By retrieving relevant SDMs to augment LLM reasoning, MARI achieves interpretability and scalability without the prohibitive cost of processing long raw sequences. Extensive experiments show MARI significantly outperforms state-of-the-art baselines on standard next-item prediction and a newly introduced Difficult Choice Prediction task, incurring low latency overhead by decoupling memory construction from online inference. Qualitative analyses reveal actionable, human-readable insights into user decision-making, marking a concrete step toward reasoning-aware recommendation systems.

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