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.

An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
May 11, 2026Yibin Wang, Yanjie Yang, Grace Melo Guerrero +2Retrieval-Augmented Generation Recommender Systems
Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Dallas, TX, USA · Department of Agricultural Economics, Texas A&M University, College Station, TX, USA
Loom: Hybrid Retrieval-Scoring Outfit Recommendation with Semantic Material Compatibility and Occasion-Aware Embedding Priors
May 11, 2026Anushree BerliaRecommender Systems Hybrid Retrieval
Reddit2Deezer: A Scalable Dataset for Real-World Grounded Conversational Music Recommendation
May 9, 2026Haven Kim, Julian McAuleyRecommender Systems Music Recommendation
University of California San Diego
Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation
May 8, 2026Zhifei Hu, Feng XiaGraph Attention Networks Multi-View Learning
Durham University, Durham, United Kingdom · University of Sheffield, Sheffield, United Kingdom
TRACE: Tourism Recommendation with Accountable Citation Evidence
May 8, 2026Zixu Zhao, Sijin Wang, Yu Hou +6Recommender Systems IR Evaluation
UNSW Sydney · University of Adelaide · Yonsei University +1
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation
May 8, 2026Hongyang Su, Beibei Kong, Lei Cheng +3Semantic ID Recommendation LLM Grounding
Platform and Content Group, Tencent, Shenzhen, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
May 8, 2026Jingyuan Zheng, Dongjing Wang, Xin Zhang +7LLM Agent Skill Retrieval LLM Agents
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China · Sanya Traditional Chinese Medicine Hospital, Sanya, 572000, China · HDU-ITMO Joint Institute, Hangzhou Dianzi University +3
Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
May 8, 2026Nipun B Nair, Tongtong Wu, Weiqing WangAutomatic Prompt Optimization Recommender Systems
Monash University, Australia
DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
May 8, 2026Xinchi Zou, Tongzhenzhi Su, Jianjun Li +4Recommender Systems Large Language Model-Based Recommendation
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China · School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China · Laboratory for Artificial Intelligence and New Forms of Education, Central China Normal University, Wuhan, China
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
May 8, 2026Shijun Li, Pranav Belligundu, Tianxin Wei +3Agentic RAG Recommender Systems
The University of Texas at Austin · University of Illinois at Chicago · Capital One AI Foundations +1
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
May 8, 2026Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9Multi-Objective Reinforcement Learning Recommender Systems
Pinterest Inc. Seattle, Washington, USA · Pinterest Inc. New York City, New York, USA · Pinterest Inc. San Francisco, California, USA +1
Bridging Textual Profiles and Latent User Embeddings for Personalization
May 7, 2026Zhaoxuan Tan, Xiang Zhai, Yan Zhu +2Sequential Recommendation RL for Recommendation
University of Notre Dame · 2Google · * This work was done while Zhaoxuan was a student researcher at Google.
Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
May 7, 2026Guanmeng Xian, Ning Yang, Philip S. YuAdversarial Training Multimodal Robustness
Sichuan University, Chengdu, China · University of Illinois at Chicago, USA
Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling
May 7, 2026Yiqing Wu, Haoming Li, Guanyu Jiang +4Domain-Adversarial Learning Conversational Search
Bytedance Beijing, China
Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems
May 5, 2026Yuan-Chi Li, Li-Chi Chen, Sung-Yi Wu +2Human Behavior Simulation Recommender Systems
Department of Computer Science and Information Engineering National Taiwan University
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
May 2, 2026Aadarsh Senapati, Neha Kujur, Vivek YelletiDynamic Graph Learning Recommender Systems
Department of Computer Science and Engineering, SRM University Andhra Pradesh, Mangalagiri, Andhra Pradesh 522502, India
Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System
May 2, 2026Qurat Ul Ain, Mohamed Amine Chatti, William Kana Tsoplefack +2Recommender Systems
Social Computing Group, Faculty of Computer Science, University of Duisburg-Essen, Germany
Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
May 2, 2026Jing Dong, Prakirt Raj Jhunjhunwala, Yash KanoriaInformation Retrieval Recommender Systems
Columbia Business School, Columbia University · Amazon.com Inc.
A Gated Hybrid Contrastive Collaborative Filtering Recommendation
Apr 29, 2026Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires +6Recommender Systems Collaborative Filtering
Institute of Computing, Federal University of Bahia, Salvador, Bahia, Brazil. · Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada.
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Apr 29, 2026Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas +2User Modeling Recommender Systems
Federal University of São Carlos Sorocaba, São Paulo, Brazil
Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
Apr 29, 2026Jiancheng Wang, Mingjia Yin, Hao Wang +1Feature Interaction Modeling Recommender Systems
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei, China
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
Apr 29, 2026Peter Müllner, Dominik Kowald, Markus Schedl +1Differential Privacy Recommender Systems
Know Center Research GmbH · Graz, Austria · Know Center Research GmbH & University of Graz +3
TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Apr 29, 2026Wei Yang, Rui Zhong, Zihan Lin +4Dynamic Graph Learning Recommender Systems
Xiaohongshu Inc. · Beijing, China
Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
Apr 28, 2026María Florencia Acosta, Rodrigo García Arancibia, Pamela Llop +2Recommender Systems Collaborative Filtering
Consejo Nacional de Invesitgaciones Científicas y Técnicas (CONICET), Santa Fe, 3000, Santa Fe, Argentina · Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral, Santa Fe, 3000, Santa Fe, Argentina · Instituto de Economía Aplicada Litoral, Universidad Nacional del Litoral, Santa Fe, 3000, Santa Fe, Argentina +2
Spectral bandits
Apr 28, 2026Tomáš Kocák, Rémi Munos, Branislav Kveton +2Multi-Armed Bandits Graph Bandits
ENS de Lyon, 15 Parvis Ren´e Descartes, 69342 Lyon, France · Inria Lille – Nord Europe, SequeL team · DeepMind Paris, 14 Rue de Londres, 75009 Paris, France +2
Modeling Behavioral Intensity and Transitions for Generative Recommendation
Apr 27, 2026Wenxuan Yang, Xiaoyang Xu, Hanyu Zhang +3Sequential Recommendation Recommender Systems
Ant Group · Fudan University
Multi-Dimensional Evaluation of Sustainable City Trips with LLM-as-a-Judge and Human-in-the-Loop
Apr 27, 2026Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl +1LLM Evaluation LLM-as-a-Judge
Technical University of Munich Munich, Germany · Polytechnic University of Bari Bari, Italy
Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Apr 27, 2026Liang Guo, Ge Song, Litao Deng +9Sequential Recommendation Training Data Curation
Meta Platforms, Inc., USA
CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
Apr 24, 2026Nilson ChapagainOff-Policy Evaluation Recommender Systems
Texas A&M University College Station, TX, USA
Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems
Apr 24, 2026Junsong Xie, Yonghui Yang, Pengyang Shao +1Sharpness-Aware Minimization Adversarial Attacks
Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei 230009, China · Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, China · Laboratory of NExT++, National University of Singapore, Singapore

May 11, 2026J/K move · Enter open · S save

An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations

Yibin Wang, Yanjie Yang, Grace Melo Guerrero, Rodolfo M. Nayga+1

Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Dallas, TX, USA · Department of Agricultural Economics, Texas A&M University, College Station, TX, USA

Retrieval-Augmented Generation Recommender Systems Large Language Model-Based Recommendation

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Diet quality is a leading determinant of chronic disease risk. Advances in artificial intelligence (AI) have enabled food recommendation systems to adapt suggestions to user preferences and health goals. However, most current systems rely on loosely curated food databases and provide limited connection to a validated index. In this study, we propose a Healthy Eating Index (HEI) informed retrieval-augmented generation (RAG) framework that combines standardized nutrition databases with large language models (LLMs) for personalized food recommendations. Our proposed method anchors retrieval in the National Health and Nutrition Examination Survey (NHANES) and the Food Patterns Equivalents Database (FPED). A food-level embedding space is constructed from FPED-derived textual descriptions. For each entity, the system computes baseline HEI scores, retrieves candidate foods for intake recommendations, and estimates the HEI impact of simple substitutions or additions. A constrained RAG pipeline instantiated with a pretrained OpenAI LLM generates personalized recommendations and sources based on nutrient profiles and HEI contributions. The simulation results showed a mean HEI improvement of 6.45, with the proportion of users HEI over 50 increasing from 45.12 to 61.26. Quantile analysis revealed consistent improved shifts across the HEI distribution. Our findings suggest that the proposed LLM-RAG-based AI systems can support more precise, explainable, and personalized nutrition guidance to improve diet quality.