Recommender Systems

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Overview

Recommender systems study how to select and order items—such as videos, products, news, or courses—for each person, helping people find relevant material in large catalogs. Common approaches include collaborative filtering and matrix factorization, sequential and graph neural models, content and multimodal representations, and two-stage retrieval followed by ranking or reranking; bandits and reinforcement learning adapt recommendations from feedback. Recent work also uses language models for conversational, generative, and explainable recommendation, often paired with structured retrieval, semantic item IDs, or offline precomputation to meet serving constraints. Key open tensions are improving cold-start and long-tail discovery while preserving accuracy, latency, privacy, fairness, diversity, and safety, and evaluating gains with realistic temporal tests and online user outcomes rather than ranking metrics alone.

Latest papers

CardsList

521 papers47 papers in the last four weeks, down 11% on the four weeks before. 0.3% of all new papers.

  1. Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation

    Apr 22, 2026Dongding Lin, Jian Wang, Yongqi Li +1Recommender SystemsLarge Language Model-Based Recommendation

    Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University

  2. AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization

    Apr 21, 2026Xidong Wu, Yue Zhuan, Ruoqiao Wei +7Multi-Agent LLM SystemsMulti-Agent System Optimization

    Google Mountain View, California, USA

  3. From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems

    Apr 21, 2026Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai +6Counterfactual EvaluationCounterfactual Explanations

    VNU University of Engineering and Technology · Delft University of Technology

  4. CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation

    Apr 21, 2026Qian Zhang, Lech Szymanski, Haibo Zhang +1Sequential RecommendationRecommender Systems

    School of Computing, University of Otago · Dunedin, New Zealand · University of New South Wales +1

  5. Spectral bandits for smooth graph functions

    Apr 20, 2026Michal Valko, Rémi Munos, Branislav Kveton +1Graph Signal ProcessingMulti-Armed Bandits

    INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · INRIA Lille - Nord Europe, SequeL team, France · Microsoft Research New England, Cambridge, MA, USA +1

  6. Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

    Apr 20, 2026Runhao Jiang, Renchi Yang, Donghao WuEmbedding CompressionRecommender Systems

    Hong Kong Baptist University · The Chinese University of Hong Kong

  7. Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data

    Apr 20, 2026Zhaohua Liang, Zhilin Wang, Renjie Cao +1Learning to RankSemantic Search

    OpenJobs AI · San Francisico, CA, USA

  8. HORIZON: A Benchmark for In-the-wild User Behaviour Modeling

    Apr 19, 2026Arnav Goel, Pranjal A Chitale, Bhawna Paliwal +2Sequential RecommendationCross-Domain Recommendation

    Carnegie Mellon University · Microsoft Research India · University of California, Berkeley

  9. From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

    Apr 18, 2026Abhirup Dasgupta, Fernando Spadea, Oshani SeneviratneKG ConstructionKnowledge Representation

    Rensselaer Polytechnic Institute, Troy NY 12180, USA

  10. Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning

    Apr 18, 2026Jiachen QianAI Agent SecurityAgent Memory Poisoning

    City University of Hong Kong

  11. Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation

    Apr 17, 2026Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1LLM PromptingSocial Bias in Language Models

    University of Zurich, Department of Informatics, Zurich, CH · New York University, Department of Sociology, New York, USA · University of Oxford, Department of Sociology, Oxford, UK +2

  12. Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

    Apr 16, 2026Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo +1Sequential RecommendationRecommender Systems

    Federal University of São Carlos · Sorocaba, São Paulo, Brazil

  13. Collaborative Filtering Through Weighted Similarities of User and Item Embeddings

    Apr 16, 2026Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo +1Ensemble LearningRecommender Systems

    Federal University of São Carlos

  14. GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation

    Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7RL for RecommendationGenerative Retrieval

    JD.com Beijing, China · Waseda University Tokyo, Japan

  15. Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion

    Apr 16, 2026Xiangrui Xiong, Hang Liang, Baiyang Chen +2AI in EducationRecommender Systems

    Accidenta

  16. CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations

    Apr 16, 2026Xiping Li, Aier Yang, Jianghong Ma +4Graph Neural NetworksRecommender Systems

    Harbin Institute of Technology, China · School of Computer Science, Wuhan University, China

  17. NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation

    Apr 16, 2026Rongyao Wang, Veronica Liesaputra, Zhiyi HuangRecommender Systems

    School of Computing, University of Otago, Dunedin 9016, New Zealand

  18. Driving Engagement in Daily Fantasy Sports with a Scalable and Urgency-Aware Ranking Engine

    Apr 15, 2026Unmesh PadalkarRecommender SystemsLearning to Rank

    Dream11

  19. TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds

    Apr 15, 2026Yifeng Zhou, Yuehong Hu, Zhixiang Feng +9Recommender SystemsSequential Recommendation

    Tencent Inc., Shenzhen, China

  20. Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization

    Apr 14, 2026Junsoo Park, Youssef Medhat, Htet Phyo Wai +2Recommender SystemsPersonalized Learning

    Georgia Institute of Technology, Atlanta, GA, USA

  21. SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling

    Apr 13, 2026Zikun Liu, Liang Luo, Qianru Li +31Inference AccelerationRecommender Systems

    Meta AI, Menlo Park, California, USA

  22. WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations

    Apr 13, 2026Aryan Singh Dalal, Maria Baloch, Asiyah Yu Lin +3Knowledge RepresentationRecommender Systems

    Department of Computer Science, Kansas State University, Manhattan, Kansas, USA · National Center for Ontological Research (NCOR), University at Buffalo, Buffalo, NY, USA · University of Texas MD Anderson Cancer Center, The University of Texas System, Houston, Texas, USA

  23. MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation

    Apr 12, 2026Hongwei XuMusic RecommendationRecommender Systems

  24. MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations

    Apr 11, 2026Abderaouf Bahi, Mourad Boughaba, Ibtissem Gasmi +2Recommender SystemsUser Preference Modeling

    Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria · Electro-Mechanical Systems Laboratory (LSEM), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria · Mathematical Modeling and Numerical Simulation Laboratory (LAM2SIN), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria

  25. From Recency Bias to Stable Convergence Block Kaczmarz Methods for Online Preference Learning in Matchmaking Applications

    Apr 11, 2026James NguyenPreference LearningRecommender Systems

    Department of Mathematics, University of California, Irvine.

  26. TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

    Apr 10, 2026Qingzhuo Wang, Leilei Wen, Juntao Chen +4Sequential RecommendationRecommender Systems

    Tongji University

  27. Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits

    Apr 9, 2026Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshminarayanan Subramanian +1Multi-Agent BanditsRecommender Systems

    Courant Institute, New York University · Indian Institute of Technology Bombay · FAIR, Meta AI

  28. DialBGM: A Benchmark for Background Music Recommendation from Everyday Multi-Turn Dialogues

    Apr 9, 2026Joonhyeok Shin, Jaehoon Kang, Yujun Lee +4Music RecommendationRecommender Systems

    Sungkyunkwan University, Republic of Korea

  29. Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems

    Apr 9, 2026Jannik Nitschke, Lukas Wegmeth, Joeran BeelRecommender SystemsEnsemble Learning

    University of Siegen, Siegen, Germany

  30. Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders

    Apr 9, 2026Jaehyun Lee, Sanghwan Jang, SeongKu Kang +1Recommender SystemsLarge Language Model-Based Recommendation

    Pohang University of Science and Technology, Pohang, Republic of Korea · Korea University, Seoul, Republic of Korea