Large Language Model-Based Recommendation

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11 papers in the last four weeks, down 15% on the four weeks before. 0.1% of all new papers.

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LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
Aug 4, 2026Eugene Lee, Oseong Choi, Byungsoo Kang +1Thompson Sampling Cold-Start Recommendation
NAVER WEBTOON South Korea
SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay
Aug 4, 2026Guilin Li, Jiaxing Zhang, Matthias Hwai Yong Tan +2Sequential Recommendation Financial Fraud Detection
Shanghai Jiao Tong University China
Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation
Jul 30, 2026Long Zhang, Hao Jiang, Sheng Yu +3Semantic ID Recommendation Disentangled Representation Learning
Kuaishou Technology Beijing, China
Hierarchical Latent Reasoning for LLM-based Recommendation
Jul 30, 2026Peiyu Hu, Siying Gu, Weihai Lu +8Sequential Recommendation RL for Language Model Reasoning
1Xi’an Jiaotong-Liverpool University · 2Xiaohongshu · 3Peking University +1
OneLatent: Latent Reasoning for Efficient Foundation Recommendation Models
Jul 29, 2026Hao Jiang, Peiru Du, Pengfei Yao +10LLM Inference Acceleration Large Language Model-Based Recommendation
Kuaishou Technology, Beijing, China
Nudging Sustainable Choices through LLM-Generated Recommendation Explanations
Jul 28, 2026Haya Halimeh, Dietmar Jannach, Oliver MüllerRecommender Systems Large Language Model-Based Recommendation
Paderborn University, Germany · University of Klagenfurt, Austria
TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
Jul 28, 2026He Ma, Chen LiuHeterogeneous GNNs Recommender Systems
The University of Sydney Camperdown, NSW, 2006, Australia · Nankai University Tianjing, China
MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation
Jul 28, 2026Jiahao Tian, Zhenkai WangLLM Reranking Recommender Systems
The University of Texas at Austin Austin, Texas, USA
Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
Jul 28, 2026Huwei Ji, Jiajie Su, Yuyuan Li +2Sequential Recommendation Cross-Domain Transfer Learning
Zhejiang University Hangzhou, China · Hangzhou Dianzi University Hangzhou, China
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Jul 28, 2026Deyao Hong, Kehan Zheng, Qian Li +3Mechanism Design Recommender Systems
Tsinghua University · Tencent Inc.
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Jul 28, 2026Shutong Qiao, Wei Yuan, Tong Chen +3Semantic ID Recommendation Recommender Systems
University of Queensland Brisbane, Australia · Computer Network Information Center, Chinese Academy of Sciences Beijing, China · Griffith University Gold Coast, Australia
MEMOIR: Temporal Behavioral Memory for Recommendation Across the Preference-Drift Spectrum
Jul 27, 2026Younggue BaeSequential Recommendation Large Language Model-Based Recommendation
Independent Researcher Vancouver, Canada
CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation
Jul 26, 2026Gengyu ZhanMemory-Augmented Language Models Recommender Systems
Shenzhen University
Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation
Jul 24, 2026Sungwook Yoo, Sewook YooRecommender Systems Large Language Model-Based Recommendation
Samsung South Korea
REPREC: Representation Driven Parameter-Efficient Recommendation System
Jul 24, 2026Harshini Kavuru, Dwipam Katariya, Giri Iyengar +3Sequential Recommendation Large Language Model-Based Recommendation
The Ohio State University, USA · Capital One, AI Foundations, USA
Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models
Jul 23, 2026Shinhwan Kang, Soo Yong Lee, Jaewon Kim +2Clinical Decision Support Large Language Model-Based Recommendation
KAIST Seoul, South Korea · KAIST Daejeon, South Korea · Korea University Seoul, South Korea
Personalized Recommendation Tool Learning via Autonomous Language Agents
Jul 22, 2026Mingdai Yang, Zhiwei Liu, Weizhi Zhang +3LLM Tool Use Recommender Systems
Univ. of Illinois Chicago Chicago, IL, USA · Microsoft Mountain View, USA · Beihang University, & Hangzhou Innovation Institute of BUAA Beijing & Hangzhou, China
PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
Jul 16, 2026Bowen Jiang, Yuan Yuan, Zhuoqun Hao +12Personal AI Agents LLM Personalization
University of Pennsylvania · Meta Recommendation Systems · MIT
Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders
Jul 11, 2026Cedric Waterschoot, Nava Tintarev, Francesco BarileHuman Preference Modeling Recommender Systems
Maastricht University Maastricht, The Netherlands
Tokenizing Numerical and Embedding Features for LLM RecSys
Jul 10, 2026Zhe Xu, Ankit Peshin, Chiyu Zhang +7Recommender Systems Large Language Model-Based Recommendation
Meta
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Jul 10, 2026Lei Shi, Di Wang, Harry Tran +16Recommender Systems Large Language Model-Based Recommendation
1Meta
Serving the Long Tail: Training-Free LLM Candidate Generation for Vacation Rental Marketplaces
Jul 10, 2026Syed Mohammed Arshad Zaidi, Eric Rincon, Shayan HassantabarSemantic Search Recommender Systems
Expedia Group Austin, Texas, USA
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
Jul 8, 2026Wachiravit Modecrua, Krittin Pachtrachai, Touchapon KraisingkornLarge Language Model-Based Recommendation Digital Twins
Amity Research and Application Center (ARAC), Amity AI Holdings Co., Ltd.
SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
Jul 7, 2026Haz Sameen Shahgir, Yufei Li, Xiaohan Wei +8CoT Distillation Language Model Distillation
University of California Riverside · Meta AI
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Jul 5, 2026Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7LLM Agent Evaluation Recommender Systems
National University of Singapore · Polytechnic University of Bari · Renmin University of China +1
LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Jul 5, 2026Hongchen Li, Bohao Wang, Jingbang Chen +5Large Language Model-Based Recommendation Attention Mechanisms
State Key Laboratory of Blockchain and Data Security, Zhejiang University. · College of Computer Science and Technology, Zhejiang University. · Zhejiang University Hangzhou, Zhejiang, China +3
Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Jul 1, 2026Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen +12Blockwise Diffusion Language Models Efficient Language Model Reasoning
Yang · 2UNC Chapel Hill · 1Meta AI
GR2 Technical Report
Jun 30, 2026Yufei Li, Zaiwei Zhang, Mingfu Liang +68RL for Language Models LLM Reranking
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
Jun 30, 2026Jiacheng Chen, Tao Zhang, Manxi Lin +26Large Language Model-Based Recommendation E-Commerce
Alibaba Taobao & Tmall Group
Beyond expert users: agents should help users construct preferences, not just elicit them
Jun 29, 2026Irena Saracay, Ludwig Schmidt, Carlos GuestrinAI Agent Benchmarks Recommender Systems
Stanford University

Aug 4, 2026, cs.IRJ/K move · Enter open · S save

LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation

Eugene Lee, Oseong Choi, Byungsoo Kang, Taeyeong Jang

NAVER WEBTOON South Korea

Thompson Sampling Cold-Start Recommendation Bayesian Inference Large Language Model-Based Recommendation User Preference Modeling

Open paper AskPDF arXiv

Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation. Despite their ability to adapt from online feedback, these methods often suffer from cold-start limitations when newly introduced arms have little or no interaction history. In our setting, the candidate arms are user-generated textual comments, whose semantic content can reveal a title's appeal before sufficient interaction feedback is available. We therefore use large language models (LLMs) to extract semantic signals from comment text and convert them into informative Bayesian priors that warm-start Thompson sampling under sparse early-stage feedback. To account for aggregate segment-level differences in response patterns, we maintain and update posteriors separately for each gender-age segment. In a real-world online A/B/C test, we compare a uniform prior with two LLM-based designs: a Gender Prior for demographic-affinity cues and a Content Prior for title-specific identity cues. The results show that LLM-based priors are most beneficial in sparse-feedback regimes -- with the largest gains emerging once a small amount of interaction evidence has accumulated -- and that prior design leads to distinct funnel-level effects. We further analyze prior-reward alignment and demographic heterogeneity, finding that click-oriented alignment is strongest for the Gender Prior and that treatment effects vary substantially across demographic segments. These findings suggest that LLM-derived priors can serve as a practical warm-start mechanism for text-rich bandit recommendation, while also revealing deployment trade-offs.

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Latest papers 126