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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TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Jul 28, 2026Ziyu Zheng, Zhengshun Du, Yaming Yang +5Semantic ID Recommendation Recommender Systems
Xidian University, Xi’an, China · Alibaba Beijing, China · University of Science and Technology of China Hefei, China
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Jul 28, 2026Shutong Qiao, Wei Yuan, Tong Chen +3Semantic ID Recommendation Recommender Systems
University of Queensland Brisbane, Australia · Computer Network Information Center, Chinese Academy of Sciences Beijing, China · Griffith University Gold Coast, Australia
Memory Layer: Train the In-Model Cache for Recommendation Models
Jul 27, 2026Liangyuan Na, Gufan Yin, Yixin Bao +19KV Caching Recommender Systems
Meta
Integrating Factual and Normative Industrial Knowledge via Constraint-Aware Graph Attention for Process Plan Recommendation
Jul 27, 2026Yuntong Chen, Yingqi Li, Yingying Xiao +5Manufacturing Graph Attention Networks
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China · State Key Laboratory of Complex Product Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, 100854, China
SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation
Jul 27, 2026Yu Cui, Yi Xu, Jiahao Wang +6Representation Collapse Transformer
Zhejiang University Hangzhou, China · Alibaba Group Beijing, China
Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders
Jul 26, 2026Ge Zhang, Jingru Cheng, Huiyuan ChenLearning to Rank Adversarial Attacks
Stanford University · Independent Researcher
Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems
Jul 26, 2026Baolei Li, Yiping Yuan, Yilin Zheng +6Semantic ID Recommendation Embedding Compression
YouTube · Google Deepmind
CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation
Jul 26, 2026Gengyu ZhanMemory-Augmented Language Models Recommender Systems
Shenzhen University
A scalable online machine learning approach for Stock Recommendation
Jul 25, 2026Harsh NagarkarDouble ML Recommender Systems
California State University Chico · Chico, California, USA
Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation
Jul 24, 2026Sungwook Yoo, Sewook YooRecommender Systems Large Language Model-Based Recommendation
Samsung South Korea
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
Jul 24, 2026Olafur Gudmundsson, Bo Zhao, Huayi Liao +15Cold-Start Recommendation Information Retrieval
Pinterest, Inc. San Francisco, USA
Efficient Recommendations via Graph Coarsening and Label Propagation
Jul 24, 2026Alessandro Sbandi, Federico Siciliano, Fabrizio SilvestriGraph Coarsening Recommender Systems
Sapienza University of Rome, Rome, Italy · TIM S.p.A.
Probabilistic Residual Learning for Online Recommendations
Jul 23, 2026Wenyuan Wang, Yusong Zhao, Zihao Xu +11Residual Learning Recommender Systems
Rutgers University · Piscataway, New Jersey, United States · Meta +4
Cardinality-Decomposed Loss: Matching Training Objectives to Relation Structure in Heterogeneous Recommendation Graphs
Jul 22, 2026Parul Maheshwari, Amulya Paruchuri, Yiqing Zou +3Pairwise Preference Learning Heterogeneous GNNs
PayPal AI · PayPal Inc. · USA
Personalized Recommendation Tool Learning via Autonomous Language Agents
Jul 22, 2026Mingdai Yang, Zhiwei Liu, Weizhi Zhang +3LLM Tool Use Recommender Systems
Univ. of Illinois Chicago Chicago, IL, USA · Microsoft Mountain View, USA · Beihang University, & Hangzhou Innovation Institute of BUAA Beijing & Hangzhou, China
Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
Jul 21, 2026Rawaa Alatrash, Mohamed Amine Chatti, Hong Yang +1Heterogeneous GNNs User Modeling
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
Jul 21, 2026Yongsen Zheng, Ruilin Xu, Guohua Wang +2Recommender Systems
Nanyang Technological University, Singapore · Digital Trust Centre Singapore · Sun Yat-sen University +3
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation
Jul 20, 2026Hanchen Yang, Kaiwen Yang, Junpeng Zhuang +7Recommender Systems Self-Improving Agents
Kuaishou Technology, Beijing, China
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Jul 20, 2026Yongsen Zheng, Ruilin Xu, Ziliang Chen +4Recommender Systems Preference Learning
1Sun Yat-sen University · 2Peng Cheng Laboratory · 5Jinan University +2
WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture
Jul 19, 2026Renqin Cai, Dawei Sun, Yuanjun Yao +8Sequential Recommendation Feature Interaction Modeling
Meta Platforms, Inc. USA
RoleMix: Unifying Sequential and Non-Sequential Features via Semantic Tokenization for Post-Click Conversion Rate Prediction
Jul 18, 2026Wenan Wang, Qin Zhao, Zhixiang LuFeature Interaction Modeling Recommender Systems
Macau Polytechnic University · University of Liverpool
Field-Aware RankMixer with Dual-Stream Bilinear Fusion for the Tencent UNI-REC Challenge
Jul 17, 2026Yufeng Zhang, Zhengqi Xu, Jiajun CuiFeature Interaction Modeling Recommender Systems
Independent Researcher · Meituan
Hard Rules, Soft Preferences: Bridging Reasoning, Learning, and Optimization for Personalized Packing Checklist Generation
Jul 17, 2026Himel Dev, Madhusudan Basak, Tanmoy Sen +2Recommender Systems Preference Learning
Tech LLC · University of Massachusetts Amherst · University of Virginia
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
Jul 15, 2026Dingsu Wang, Filip Ryzner, Kelly He +12Reward Modeling Learning to Rank
Pinterest San Francisco, CA, USA
OrDA: Orthogonal Disentanglement of Access Habits Framework for Homepage Marketing Block Recommendations
Jul 15, 2026Lingxiao Zhang, Xiaobo Li, Tao XuRepresentation Disentanglement Recommender Systems
Ant Group · Hangzhou, Zhejiang, China
Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents
Jul 15, 2026Jiwen Zhou, Xiang Liu, Mingming Li +5Algorithmic Auditing Recommender Systems
Institute of Information Engineering, Chinese Academy of Sciences, China · School of Cyber Security, University of Chinese Academy of Sciences, China
Privacy Preserving Recommender Systems Balancing Personalization with Privacy
Jul 14, 2026Ranjeet K Jha, Venkata Suresh GummadilliDifferential Privacy Recommender Systems
College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712 USA
ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark
Jul 14, 2026Minh Hoang NguyenCold-Start Recommendation Recommender Systems
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jul 14, 2026Jiaxing Qu, Yilin Chen, Junpeng Hou +4Recommender Systems
Pinterest Inc. San Francisco, CA, USA
SlimPer: Make Personalization Model Slim and Smart
Jul 14, 2026Siqi Wang, Xianjie Chen, Shaofeng Deng +42Learning to Rank Memory-Efficient Inference

Jul 28, 2026, cs.IRJ/K move · Enter open · S save

TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation

Ziyu Zheng, Zhengshun Du, Yaming Yang, Bin Tong+4

Xidian University, Xi’an, China · Alibaba Beijing, China · University of Science and Technology of China Hefei, China

Semantic ID Recommendation Recommender Systems Generative Recommendation Topological Representation Learning

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Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing methods typically regard SIDs as independent discrete symbols, while often overlooking the topology of the learned semantic ID space. We identify a structural mismatch between tokenization and generation: the tokenizer learns a structured code space with semantic neighborhood relations, whereas the generator consumes semantic ID tokens as independent categorical symbols. Consequently, item relatedness is reduced to exact semantic ID overlap, making it difficult to identify semantically similar items whose semantic IDs do not overlap. To address this issue, we propose TopoGR, a topology-preserving generative recommendation framework based on Bit-decomposable Semantic ID(Binary SID). Each Binary SID is learned in a bit-decomposable form and can be deterministically converted to a standard integer SID, while exposing an explicit Hamming geometry. TopoGR exploits this topology at three stages: binary SID features preserve Hamming proximity at the input layer; Hamming soft targets inject topology-aware supervision; and Hamming-consistent reranking aligns candidate items with the predicted binary prototype during inference. We further verify that the Hamming topology can capture item relatedness beyond exact SID matching. Experiments on four benchmark datasets show that TopoGR consistently outperforms existing state-of-the-art baselines in recommendation performance.

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