Recommender Systems

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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

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

Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Mar 7, 2026Kai Cheng, Hao Wang, Wei Guo +4Online Knowledge Distillation Recommender Systems
University of Science and Technology of China, Hefei, China · Huawei, Shanghai, China · Shanghai Jiao Tong University, Shanghai, China +1
Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation
Mar 6, 2026Ruixuan Sun, Matthew Zent, Minzhu Zhao +3Recommender Systems LLM Personalization
Grouplens Research, University of Minnesota, Minneapolis, Minnesota, United States · Northwestern University, Evanston, Illinois, United States
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Mar 4, 2026Yun Lu, Xiaoyu Shi, Hong Xie +2Fairness in Recommender Systems Recommender Systems
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences Chongqing, China · The First Affiliated Hospital, University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong, Hong Kong
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Mar 4, 2026Pengfei Tong, Siyuan Chen, Chenwei Zhang +4Recommender Systems Learning to Rank
ByteDance
AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
Mar 4, 2026Yunxiao Shi, Wujiang Xu, Tingwei Chen +7Agent Benchmarks LLM Agents
University of Technology Sydney · Rutgers University · University of New South Wales +2
SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems
Mar 3, 2026Haochang Hao, Yifan Xu, Xinzhuo Li +2Recommender Systems Large Language Model-Based Recommendation
University of Illinois at Chicago Chicago, IL, USA · University of Notre Dame South Bend, IN, USA · Amazon Santa Clara, CA, USA
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
Mar 3, 2026Chongjun Xia, Xiaoyu Shi, Hong Xie +3Long-Tail Recommendation Fairness in Recommender Systems
Chinese Academy of Sciences · University of Technology Sydney · University of Science and Technology of China
Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation
Mar 3, 2026Yangchen Zeng, Jinze WangRecommender Systems Semantic ID Recommendation
Sensory-Aware Sequential Recommendation via Review-Distilled Representations
Mar 3, 2026Yeo Chan Yoon, Chanjun Park, Kyuhan KohRecommender Systems Sequential Recommendation
Jeju National University, Jeju, Jeju, Korea · Soongsil University, Seoul, Korea · California State University Stanislaus, Turlock, USA
OmniFashion: Towards Generalist Fashion Intelligence via Multi-Task Vision-Language Learning
Mar 3, 2026Zhengwei Yang, Andi Long, Hao Li +3Vision-Language Models Large Vision-Language Models
National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University · Harbin Institute of Technology
AlphaFree: Recommendation Free from Users, IDs, and GNNs
Mar 3, 2026Minseo Jeon, Junwoo Jung, Daewon Gwak +1Recommender Systems Large Language Model-Based Recommendation
Soongsil University Seoul, Republic of Korea
FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation
Mar 3, 2026Chao Feng, Yuanhao Pu, Chenghao Zhang +5Efficient Inference Recommender Systems
Kuaishou Technology, Beijing, China
SOLAR: SVD-Optimized Lifelong Attention for Recommendation
Mar 3, 2026Chenghao Zhang, Chao Feng, Yuanhao Pu +5Recommender Systems Sequential Recommendation
Kuaishou Technology. · Unaffiliated.
MealRec: Multi-granularity Sequential Modeling via Hierarchical Diffusion Models for Micro-Video Recommendation
Mar 2, 2026Xinxin Dong, Haokai Ma, Yuze Zheng +3Recommender Systems Sequential Recommendation
National University of Defense Technology Changsha, China · National University of Singapore Singapore, Singapore
GeMi: A Graph-based, Multimodal Recommendation System for Narrative Scroll Paintings
Mar 1, 2026Haimonti Dutta, Pruthvi Moluguri, Jin Dai +1Recommender Systems Multimodal Recommendation
Department of Management Science and Systems, The State University of New York, Jacobs Management Center, Buffalo, 14260, NY, USA. · Institute for Artificial Intelligence and Data Science, The State University of New York, Lockwood Hall, Buffalo, 14260, NY, USA. · Department of Computer Science and Engineering, The State University of New York, Davis Hall, Buffalo, 14260, NY, USA. +1
RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation
Feb 28, 2026Jin Zeng, Yupeng Qi, Hui Li +4Recommender Systems Large Language Model-Based Recommendation
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China · Department of Computer Science and Technology, Xiamen University, Xiamen, Fujian, China · Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, Guangdong, China +2
Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
Feb 28, 2026Wenhao Zheng, Wang Lu, Fangshuang Tang +4Cross-Domain Recommendation Cold-Start Recommendation
Mircosoft Corp, Redmond, WA, USA
MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation
Feb 28, 2026Rong Shan, Aofan Yu, Bo Chen +7Recommender Systems Muon Optimizer
Shanghai Jiao Tong University, Shanghai, China · Kuaishou Technology, Beijing, China
From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Feb 26, 2026Ruochen Yang, Xiaodong Li, Jiawei Sheng +6Recommender Systems Sequential Recommendation
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, UCAS · Kuaishou Technology
RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems
Feb 26, 2026Runpeng Cui, Zhipeng Sun, Chi Lu +1Residual VQ Recommender Systems
Kuaishou Technology Beijing, China · Beijing, China
SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
Feb 26, 2026Yang Yu, Lei Kou, Huaikuan Yi +6Recommender Systems Generative Recommendation
Alibaba International Digital Commercial Group, Hangzhou, China
Generative Data Transformation: From Mixed to Unified Data
Feb 26, 2026Jiaqing Zhang, Mingjia Yin, Hao Wang +6Cross-Domain Recommendation Data Augmentation
University of Science and Technology of China, Hefei, China · Beijing University of Posts and Telecommunications, Beijing, China · Huawei Noah’s Ark Lab, Shenzhen, China
Generative Recommendation for Large-Scale Advertising
Feb 26, 2026Ben Xue, Dan Liu, Lixiang Wang +27RL for Recommendation Recommender Systems
Kuaishou Technology, Beijing, China
Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
Feb 25, 2026Fernando Spadea, Oshani SeneviratneRecommender Systems Large Language Model-Based Recommendation
Rensselaer Polytechnic Institute, Troy, NY, USA
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
Feb 25, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Recommender Systems
Department of Electrical Engineering & Computer Science University of Arkansas Fayetteville, USA
Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
Feb 25, 2026Yuchun Tu, Zhiwei Li, Bingli Sun +2Recommender Systems Federated Learning
Beihang University, Beijing, China · University of Technology Sydney, Sydney, Australia · University of Edinburgh, Edinburgh, UK
Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing
Feb 25, 2026Deogyong Kim, Junseong Lee, Jeongeun Lee +4Recommender Systems Large Language Model-Based Recommendation
Yonsei University, Seoul, South Korea · NAVER, Seongnam, South Korea
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Feb 24, 2026Kun Yuan, Junyu Bi, Daixuan Cheng +5Sequential Recommendation Hierarchical Representation Learning
Alibaba Group · Beijing, China · Renmin University
Generative Pseudo-Labeling for Pre-Ranking with LLMs
Feb 24, 2026Junyu Bi, Xinting Niu, Daixuan Cheng +4Pseudo-Labeling Recommender Systems
Alibaba Group Beijing, China · Renmin University Beijing, China
A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
Feb 24, 2026Nikita Baidya, Bidyut Kr. Patra, Ratnakar DashFairness in Recommender Systems Recommender Systems
Pattern Recognition Laboratory, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India. · Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, India.

Mar 7, 2026J/K move · Enter open · S save

Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

Kai Cheng, Hao Wang, Wei Guo, Weiwen Liu+3

University of Science and Technology of China, Hefei, China · Huawei, Shanghai, China · Shanghai Jiao Tong University, Shanghai, China+1

Online Knowledge Distillation Recommender Systems Learning to Rank Efficient Inference Autoregressive Generation Knowledge Distillation

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Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked lists. However, their practical deployment still faces two key challenges: (1) an inherent conflict between achieving high generation quality and ensuring low-latency inference, making it difficult to balance the two, and (2) insufficient interaction between user and item features in existing methods. To address these challenges, we propose a novel Personalized Semi-Autoregressive with online knowledge Distillation (PSAD) framework for reranking. In this framework, the teacher model adopts a semi-autoregressive generator to balance generation quality and efficiency, while its ranking knowledge is distilled online into a lightweight scoring network during joint training, enabling real-time and efficient inference. Furthermore, we propose a User Profile Network (UPN) that injects user intent and models interest dynamics, enabling deeper interactions between users and items. Extensive experiments conducted on three large-scale public datasets demonstrate that PSAD significantly outperforms state-of-the-art baselines in both ranking performance and inference efficiency.