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

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

  1. Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

    Apr 9, 2026Cathy Jiao, Juan Elenter, Praveen Ravichandran +7Data SelectionRecommender Systems

    Carnegie Mellon University · Spotify

  2. NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining

    Apr 8, 2026Zhida Jiang, Zhaolong Xing, Huichao Chai +12Distributed TrainingPipeline Parallelism

    Huawei

  3. CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation

    Apr 8, 2026Yanan Cao, Ashish Ranjan, Sinduja Subramaniam +3Recommender SystemsSequential Recommendation

    Walmart Global Tech, Sunnyvale, CA, USA

  4. The Unreasonable Effectiveness of Data for Recommender Systems

    Apr 7, 2026Youssef AbdouRecommender SystemsCollaborative Filtering

  5. LLM Biases

    Apr 7, 2026Jinhui Han, Ming Hu, Xilin ZhangPopularity Bias in Recommender SystemsRecommender Systems

    Guanghua School of Management, Peking University, Beijing 100871, China · Rotman School of Management, University of Toronto, Toronto, Ontario, Canada M5S 3E6

  6. Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation

    Apr 6, 2026Jiazhou Liang, Yifan Simon Liu, David Guo +3Recommender Systems

    University of Toronto, Toronto, Canada · University of Waterloo, Waterloo, Canada · 3Vector Institute of Artificial Intelligence, Toronto, Canada

  7. CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation

    Apr 6, 2026Zezhong Fan, Ziheng Chen, Luyi Ma +5Popularity Bias in Recommender SystemsRecommender Systems

    Walmart Global Tech, Sunnyvale, USA · Stony Brook University, NY, USA, Stony Brook, USA

  8. Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation

    Apr 4, 2026Zhifu Wei, Yizhou Dang, Guibing Guo +2Recommender SystemsSequential Recommendation

    Northeastern University, Shenyang, China · Tianjin University, Tianjin, China · Singapore University of Technology and Design, Singapore, Singapore

  9. Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation

    Apr 3, 2026Jinze Wang, Yangchen Zeng, Tiehua Zhang +5Recommender SystemsMultimodal Recommendation

    Tongji University · Swinburne University of Technology · Southeast University +4

  10. Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization

    Apr 2, 2026Ansel Kaplan Erol, Seohee Yoon, Keenan Hom +1Job-Candidate MatchingRecommender Systems

    Georgia Institute of Technology, Atlanta, GA, USA

  11. Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty

    Apr 2, 2026Enock O. Ayiku, Evelyn Osei, Emebo OnyekaRecommender SystemsFairness in Recommender Systems

    University of Massachusetts Boston, Boston, MA, USA · Kansas State University, Manhattan, KS, USA · Virginia Tech, Blacksburg, VA, USA

  12. Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges

    Apr 2, 2026Srivaths Ranganathan, Abhishek Dharmaratnakar, Anushree Sinha +1Recommender SystemsMulti-Agent Collaboration

    Google LLC, Mountain View, USA · Google LLC, San Bruno, USA

  13. Towards Position-Robust Talent Recommendation via Large Language Models

    Apr 2, 2026Silin Du, Hongyan LiuRecommender SystemsLarge Language Model-Based Recommendation

    Department of Management Science and Engineering, Tsinghua University, Beijing, China.

  14. TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

    Apr 2, 2026Zhanting Zhou, KaHou Tam, Zeyu Ma +2Recommender SystemsMultimodal Recommendation

    University of Electronic Science and Technology of China Chengdu, China · University of Macau Macau, Macao

  15. Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

    Apr 2, 2026Tianhao Shi, Yang Zhang, Xiaoyan Zhao +8Social Media AnalysisAI Governance

    University of Science and Technology of China, Hefei, China. · National University of Singapore, Singapore. · Kuaishou Technology, Beijing, China. +1

  16. UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems

    Apr 1, 2026Mingming Ha, Guanchen Wang, Linxun Chen +9Recommender SystemsFeature Interaction Modeling

    Kuaishou Technology, Beijing, China

  17. Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

    Mar 31, 2026Hejin Huang, Jusheng Zhang, Kaitong Cai +2Recommender SystemsDirect Preference Optimization

    Sun Yat-sen University · Snap Inc

  18. With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems

    Mar 30, 2026Giovanni De Toni, Cristian Consonni, Erasmo Purificato +2Recommender SystemsAdversarial Attacks

    Fondazione Bruno Kessler, Italy · European Commission, Joint Research Centre (JRC)

  19. From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

    Mar 30, 2026Yu Xie, Ying QiHuman-AI InteractionRecommender Systems

  20. Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange

    Mar 29, 2026Yin Cheng, Liao Zhou, Xiyu Liang +7Recommender SystemsLearning to Rank

    Shopee

  21. Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization

    Mar 29, 2026Canran Xiao, Liwei HouRecommender SystemsUser Preference Modeling

    Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong, China · Hunan University Changsha, Hunan, China

  22. Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System

    Mar 26, 2026Qurat Ul Ain, Mohamed Amine Chatti, Nasim Yazdian Varjani +2Explainable RecommendationExplanation Evaluation

    University of Duisburg-Essen Duisburg, Germany · RWTH Aachen University Aachen, Germany

  23. MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

    Mar 26, 2026Ranxu Zhang, Junjie Meng, Ying Sun +5Recommender Systems

    School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China · Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · RMIT University, Melbourne, Australia +2

  24. Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems

    Mar 26, 2026Jiang Liu, John Martabano Landy, Yao Xuan +14Recommender SystemsCTR Prediction

    Meta AI, Menlo Park, California, USA

  25. Exploring How Fair Model Representations Relate to Fair Recommendations

    Mar 25, 2026Bjørnar Vassøy, Benjamin Kille, Helge LangsethFairness in Recommender SystemsAlgorithmic Fairness

    Norwegian University of Science and Technology, Trondheim, Norway

  26. LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

    Mar 25, 2026Ryogo Hishikawa, Ichiro Kataoka, Shinya YudaLLM-Assisted AnnotationRecommender Systems

    Hitachi, Ltd. Research and Development Group Hitachi, Ibaraki, Japan · Hitachi Power Solutions Co., Ltd. Hitachi, Ibaraki, Japan

  27. Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters

    Mar 24, 2026Nan Cui, Wendy Hui Wang, Yue NingFairness in Recommender SystemsDemographic Bias in Language Models

    Department of Computer Science, Stevens Institute of Technology, Hoboken NJ, USA

  28. One Model, Two Markets: Bid-Aware Generative Recommendation

    Mar 23, 2026Yanchen Jiang, Zhe Feng, Christopher P. Mah +2Recommender SystemsGenerative Recommendation

    Harvard University · Work done during an internship at Google Research · Google Research

  29. PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking

    Mar 23, 2026Wei Zhou, Wuyang Li, Junkai Ji +5Recommender SystemsPreference Learning

    Shenzhen University, Shenzhen, China · Shenzhen Technology University, Shenzhen, China

  30. MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction

    Mar 23, 2026Yunchi Yang, Longlong Li, Jianliang Wu +1Sequential RecommendationRecommender Systems

    School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · School of Mathematics, Shandong University, Jinan 250100, China · Data Science Institute, Shandong University, Jinan 250100, China