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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Bridging Textual Profiles and Latent User Embeddings for Personalization
May 7, 2026Zhaoxuan Tan, Xiang Zhai, Yan Zhu +2Sequential Recommendation Recommender Systems
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
Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
Apr 22, 2026Dongding Lin, Jian Wang, Yongqi Li +1Recommender Systems Large Language Model-Based Recommendation
Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University
AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
Apr 21, 2026Xidong Wu, Yue Zhuan, Ruoqiao Wei +7Multi-Agent LLM Systems Multi-Agent System Optimization
Google Mountain View, California, USA
From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
Apr 21, 2026Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai +6Counterfactual Evaluation Counterfactual Explanations
VNU University of Engineering and Technology · Delft University of Technology
CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation
Apr 21, 2026Qian Zhang, Lech Szymanski, Haibo Zhang +1Sequential Recommendation Recommender Systems
School of Computing, University of Otago · Dunedin, New Zealand · University of New South Wales +1
Spectral bandits for smooth graph functions
Apr 20, 2026Michal Valko, Rémi Munos, Branislav Kveton +1Graph Signal Processing Multi-Armed Bandits
INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · INRIA Lille - Nord Europe, SequeL team, France · Microsoft Research New England, Cambridge, MA, USA +1
Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
Apr 20, 2026Runhao Jiang, Renchi Yang, Donghao WuEmbedding Compression Recommender Systems
Hong Kong Baptist University · The Chinese University of Hong Kong
Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
Apr 20, 2026Zhaohua Liang, Zhilin Wang, Renjie Cao +1Learning to Rank Semantic Search
OpenJobs AI · San Francisico, CA, USA
HORIZON: A Benchmark for In-the-wild User Behaviour Modeling
Apr 19, 2026Arnav Goel, Pranjal A Chitale, Bhawna Paliwal +2Sequential Recommendation User Modeling
Carnegie Mellon University · Microsoft Research India · University of California, Berkeley
From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems
Apr 18, 2026Abhirup Dasgupta, Fernando Spadea, Oshani SeneviratneKG Construction Knowledge Representation
Rensselaer Polytechnic Institute, Troy NY 12180, USA
Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning
Apr 18, 2026Jiachen QianAI Agent Security Agent Memory Poisoning
City University of Hong Kong
Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Apr 17, 2026Nicolò Pagan, Christopher Barrie, Chris Andrew Bail +1LLM Prompting Social Bias in Language Models
University of Zurich, Department of Informatics, Zurich, CH · New York University, Department of Sociology, New York, USA · University of Oxford, Department of Sociology, Oxford, UK +2

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Bridging Textual Profiles and Latent User Embeddings for Personalization

Zhaoxuan Tan, Xiang Zhai, Yan Zhu, Meng Jiang+1

University of Notre Dame · 2Google · * This work was done while Zhaoxuan was a student researcher at Google.

Sequential Recommendation Recommender Systems Large Language Model-Based Recommendation User Preference Modeling

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Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typically employ either supervised latent user embeddings, which are effective for retrieval but difficult to interpret, or textual user profiles, which are interpretable but challenging to optimize for downstream utility due to lack of direct supervision. To bridge this gap, we present BLUE, a reinforcement learning framework that unifies these two forms of user representation by aligning language-based user profiles with embedding-based recommendation objectives. Given a user interaction history, BLUE leverages a profiler Large Language Model (LLM) to generate textual profiles, while an embedding model provides reward signals. This encourages the resulting textual representations to move closer to positive items and farther from negative ones in the embedding space. We further introduce a text-space supervision signal based on next-item prediction, ensuring the learned profiles remain both semantically meaningful and highly effective for downstream retrieval. Experiments on Amazon Reviews 2023 and Google Local Reviews in zero-shot sequential recommendation settings demonstrate that BLUE consistently outperforms strong baselines under both frozen and trainable embedding conditions. Notably, BLUE achieves clear gains in cross-domain transfer, highlighting the strong generalization ability of the learned user profiles. Furthermore, these generated profiles provide superior personalized context for question answering compared to raw user histories or alternative profile optimization methods. Overall, these results show that BLUE provides an effective way to unify interpretable textual profiling with discriminative latent embeddings for personalization.

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