Recommender Systems

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32 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

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Stay or Stray - A Dynamical Systems Viewpoint of Popularity Bias
Aug 11, 2026Sarvesh Shashidhar, Lankireddy Prabhat, Arpit Agarwal +3Algorithmic Bias Recommender Systems
Centre of Machine Intelligence & Data Science Indian Institute of Technology, Bombay Mumbai, Maharashtra, India · Electrical & Computer Engineering Dept. Georgia Institute of Technology Atlanta, GA, USA · Dept. of Computer Science & Engineering Indian Institute of Technology, Bombay Mumbai, Maharashtra, India +3
DualSpectralCF: Training-Free Sign-Aware Spectral Collaborative Filtering
Aug 10, 2026Guanqun Yang, Tong Qi, Xiaoxue HanRecommender Systems Spectral Methods
Stevens Institute of Technology Hoboken, NJ, USA · University of Maryland, College Park College Park, MD, USA
TRACE: Trustworthy Retrieval-Augmented Conversational Engine
Aug 10, 2026Touseef Hasan, Laila Cure, Souvika SarkarRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
Wichita State University, Wichita, KS, USA
UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models
Aug 9, 2026Lei Xin, Bin Gu, Peize Li +8Mixture of Experts Mixture-of-Experts Inference
Kuaishou Technology · Hohai University · Wuhan University +4
Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders
Aug 8, 2026Chan Aristella Lu, Arya Fayyazi, Junhao Zhang +6LLM Auditing Language Model Bias Evaluation
University of Georgia · University of Southern California · Carnegie Mellon University +1
HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection
Aug 8, 2026Alireza Joonbakhsh, Arda Canser Adalı, Slinger Jansen +2Model Selection Recommender Systems
Shiraz University · Shiraz University, Shiraz, Iran · Utrecht University +3
Do LLM Recommenders Know When They're Hallucinating? Auditing Confidence Calibration in Catalog Faithfulness
Aug 7, 2026Srijith RavikumarLLM Auditing Language Model Calibration
Amazon.com LLC Seattle, Washington, USA
Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training
Aug 7, 2026Oseong Choi, Hoeinn Kim, Jihoon Lee +2Recommender Systems RL Post-Training
NAVER WEBTOON Seongnam-si, Republic of Korea
Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
Aug 6, 2026Ziyun Xu, Bosen Ding, Yue Zhang +10LLM Reranking Recommender Systems
Meta Platforms Menlo Park, California, USA
Weather- and Location-Aware Agentic Dining Recommendation: Leveraging LLM World Knowledge for Region-Sensitive Contextual Reasoning
Aug 5, 2026Kadharmoideen FadurudeenLLM Agents Recommender Systems
Independent Researcher
A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing
Aug 5, 2026Zhuohang Jiang, Yuxin Chen, Yongsen Pan +6Retrieval-Augmented Generation Recommender Systems
The Hong Kong Polytechnic University Hong Kong SAR, China · Kuaishou Technology Beijing, China · Southwest Jiaotong University Chengdu, SiChuan, China
Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting
Aug 5, 2026Xiaoyi Gu, Julia Tavares, Eder Santana +3Learning to Rank Recommender Systems
Twitch Interactive, San Francisco, California, USA · Amazon Prime Video, Sunnyvale, California, USA
Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections
Aug 4, 2026Aadit Barua, Leijie Wang, Amy X. ZhangSocial Media Analysis Recommender Systems
University of Washington Seattle, Washington, United States
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
Aug 4, 2026Qianqian Wang, Wenwu Gong, Yunshan Li +3Latent Variable Models OOD Generalization
Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Southern University of Science and Technology, Shenzhen, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China · School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity
Aug 4, 2026Anjun Hu, Hanting Xie, Saranya Govindan +2Multi-Agent Systems Recommender Systems
University of Oxford · Amazon
X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking
Aug 3, 2026Meenakshi Rajpurohit, Jainish PatelGraph Retrieval-Augmented Generation LLM Reranking
Dept. of Computer Engineering San Jose State University San Jose, CA, USA
Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test
Aug 2, 2026Kong Wang, Zhongke He, Xiang Chen +4Sequential Recommendation Algorithmic Auditing
Hunan University · Dalian University of Technology · Tongji University +3
RecSys Factory: Bounding LLM Agent Autonomy to Decision Points in the Industrial Recommender Lifecycle
Jul 31, 2026Dongyang Ao, Kaixiang Fang, Shijie XuLLM Agent Orchestration LLM Agents
FiT, Tencent Shenzhen, China
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Jul 31, 2026Haoran Ling, Yuecheng Li, Zeyu Song +5Large Language Model-Guided Optimization Agent Harness Optimization
Georgia Institute of Technology · Kuaishou Technology, China
Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform
Jul 31, 2026Seungho Han, Byeongchang Kim, Jin YuContrastive Learning User Modeling
Danggeun Market Inc. (Karrot) · Seoul, Korea
Diversifying Personalized Research Ideation against AI-Induced Homogenization
Jul 30, 2026Rui Xu, Yunke Wang, Linwei Tao +2AI-Assisted Scientific Research Recommender Systems
Wuhan University · The University of Sydney
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
ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
Jul 30, 2026Yuxin Chen, Liang Luo, Buyun Zhang +44Recommender Systems
Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study
Jul 30, 2026Di Bai, Jintao Liu, Zhenwei Tang +3Learning to Rank Multi-Task Learning
Google LLC Mountain View, CA, USA · Google LLC Toronto, ON, Canada · Google LLC New York, NY, USA +1
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Jul 29, 2026Ziwei Li, Shuyao Li, Xufeng Cai +12Learning to Rank Recommender Systems
Meta Platforms, Inc. ∗USA, †Singapore
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Jul 29, 2026Finn HertschCold-Start Recommendation Recommender Systems
DHBW Ravensburg, School of Business, Data Science and AI, Marienplatz 2, 88212 Ravensburg, Germany
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
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.

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Stay or Stray - A Dynamical Systems Viewpoint of Popularity Bias

Sarvesh Shashidhar, Lankireddy Prabhat, Arpit Agarwal, D. Manjunath+2

Centre of Machine Intelligence & Data Science Indian Institute of Technology, Bombay Mumbai, Maharashtra, India · Electrical & Computer Engineering Dept. Georgia Institute of Technology Atlanta, GA, USA · Dept. of Computer Science & Engineering Indian Institute of Technology, Bombay Mumbai, Maharashtra, India+3

Algorithmic Bias Recommender Systems

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Popularity bias in recommendation systems arises when a majority user class generates disproportionate interaction data, causing the system to increasingly favour it while degrading recommendation quality for niche users. While extensive empirical evidence of popularity bias exists, the dynamics leading to its emergence are not well understood. In this work, we study the coupled evolution of recommender model updates and user engagement through the lens of dynamical systems. We formulate a stochastic process and analyse its asymptotic behaviour through an ordinary differential equation (ODE) framework grounded in two-time-scale stochastic approximation. We characterise the equilibrium points of this dynamical system, and derive conditions under which popularity bias is provably emergent, as well as conditions under which symmetric retention of all user classes is possible. We conduct experiments on synthetic data and real-world production logs derived from a large-scale commercial music recommendation platform to validate our theoretical results.

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