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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Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts
Apr 16, 2026Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo +1Sequential Recommendation Recommender Systems
Federal University of São Carlos · Sorocaba, São Paulo, Brazil
Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
Apr 16, 2026Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo +1Ensemble Learning Recommender Systems
Federal University of São Carlos
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7Generative Retrieval Recommender Systems
JD.com Beijing, China · Waseda University Tokyo, Japan
Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion
Apr 16, 2026Xiangrui Xiong, Hang Liang, Baiyang Chen +2AI in Education Recommender Systems
Accidenta
CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
Apr 16, 2026Xiping Li, Aier Yang, Jianghong Ma +4Graph Neural Networks Recommender Systems
Harbin Institute of Technology, China · School of Computer Science, Wuhan University, China
NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation
Apr 16, 2026Rongyao Wang, Veronica Liesaputra, Zhiyi HuangRecommender Systems
School of Computing, University of Otago, Dunedin 9016, New Zealand
Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation
Apr 6, 2026Jiazhou Liang, Yifan Simon Liu, David Guo +3Recommender Systems
University of Toronto, Toronto, Canada · University of Waterloo, Waterloo, Canada · 3Vector Institute of Artificial Intelligence, Toronto, Canada
TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning
Apr 2, 2026Zhanting Zhou, KaHou Tam, Zeyu Ma +2Recommender Systems Multimodal Recommendation
University of Electronic Science and Technology of China Chengdu, China · University of Macau Macau, Macao
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
Mar 26, 2026Ranxu Zhang, Junjie Meng, Ying Sun +5Recommender Systems
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China · Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · RMIT University, Melbourne, Australia +2
MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction
Mar 23, 2026Yunchi Yang, Longlong Li, Jianliang Wu +1Sequential Recommendation Recommender Systems
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · School of Mathematics, Shandong University, Jinan 250100, China · Data Science Institute, Shandong University, Jinan 250100, China
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
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
Modality-Guided Mixture of Structured Experts with Entropy-Triggered Routing for Multimodal Recommendation
Feb 24, 2026Ji Dai, Quan Fang, DeSheng CaiSparse Mixture-of-Experts Recommender Systems
Beijing University of Posts and Telecommunications, China · Beijing University of Posts and Telecommunications, Beijing, China · Tianjin University of Technology, China
Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
Feb 19, 2026Ziliang Zhao, Bi Xue, Emma Lin +16Linear Probing Recommender Systems
Meta Platforms, Inc., Menlo Park, CA, USA. · OpenAI, San Francisco, CA, USA.
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
Feb 13, 2026Sein Kim, Sangwu Park, Hongseok Kang +6Large Language Model-Guided Optimization LLM-Based Program Synthesis
KAIST · Microsoft
CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation
Feb 10, 2026Zhenyu Yu, Chunlei Meng, Yangchen Zeng +3Sequential Recommendation Recommender Systems
College of Computer Science and Artificial Intelligence Fudan University Shanghai, China · College of Intelligent Robotics and Advanced Manufacturing Fudan University Shanghai, China · School of Cyber Science and Engineering Southeast University Nanjing, China +2
GR2: Generative Reasoning Re-ranker
Feb 8, 2026Mingfu Liang, Yufei Li, Jay Xu +20RL for Language Models LLM Reranking
Meta AI
xGR: Efficient Generative Recommendation Serving at Scale
Dec 12, 2025Qingxiao Sun, Tongxuan Liu, Shen Zhang +13LLM Serving LLM Inference Acceleration
Beihang University · JD Company · University of Science and Technology Beijing +1
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
Dec 11, 2025Ziwei Liu, Yejing Wang, Wanyu Wang +6Semantic ID Recommendation Sequential Recommendation
City University of Hong Kong Hong Kong, China · Tongji University Shanghai, China · Xi’an Jiaotong University Xi’an, China +3
Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems
Oct 23, 2025Fiza Husain, Anson Bastos, Anjaly Parayil +4Graph Attention Networks Heterogeneous GNNs
Microsoft
LIME: Link-based User-item Interaction Modeling with Decoupled XOR Attention for Efficient Test Time Scaling
Oct 21, 2025Yang Liu, Yunjiang Jiang, Ayush Agarwal +3Recommender Systems Efficient Inference
Meta
EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation
Aug 22, 2025Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2Cross-Modal Alignment Multimodal Embedding
College of Computing and Data Science, Nanyang Technological University, Singapore
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Jun 12, 2025Hongyu Yao, Zijin Hong, Hao Chen +6Graph Representation Learning Multi-Task Learning
The Hong Kong Polytechnic University Hong Kong SAR, China · National University of Defense Technology Changsha, China · Beihang University Beijing, China
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
Mar 12, 2025Guanrong Li, Kuo Tian, Jinnan Qi +4Recommender Systems Large Language Model-Based Recommendation
Nanjing University Nanjing, Jiangsu, China
Impatient Bandits: Optimizing for the Long-Term Without Delay
Jan 14, 2025Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek +2Multi-Armed Bandits Sequential Recommendation
Imperial College London · University of Manchester · Spotify +2
RecKG: Knowledge Graph for Recommender Systems
Jan 7, 2025Junhyuk Kwon, Seokho Ahn, Young-Duk SeoKG Construction Recommender Systems
Inha University Incheon, South Korea
Towards Reliable Recommender Systems for Rating Data
Dec 30, 2024Aurore Archimbaud, Andreas Alfons, Ines WilmsAdversarial Attacks Recommender Systems
TBS Business School · Erasmus University Rotterdam · Maastricht University
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
Sep 30, 2024Xinfeng Wang, Jin Cui, Fumiyo Fukumoto +1Sequential Recommendation Feature Interaction Modeling
Graduate School of Engineering University of Yamanashi, Kofu, Japan · Interdisciplinary Graduate School University of Yamanashi, Kofu, Japan
Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation
Dec 18, 2023Yang Li, Kangbo Liu, Yaoxin Wu +2Recommender Systems Hypergraph Neural Networks
Northwestern Polytechnical University, China · Nanyang Technological University, Singapore

Apr 16, 2026, cs.IRJ/K move · Enter open · S save

Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo, Tiago A. Almeida

Federal University of São Carlos · Sorocaba, São Paulo, Brazil

Sequential Recommendation Recommender Systems User Preference Modeling

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Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), a family of lightweight embedding models that integrates temporal proximity directly into the representation learning process. Unlike approaches that apply global time constraints, TAI2Vec is user-adaptive, tailoring its temporal definitions to individual interaction paces. We propose two complementary strategies: TAI2Vec-Disc, which utilizes personalized anomaly detection to dynamically segment interactions into semantic sessions, and TAI2Vec-Cont, which employs continuous, user-specific decay functions to weigh item relationships based on their relative temporal distance. Experimental results across eight diverse datasets demonstrate that TAI2Vec consistently produces more accurate and behaviorally grounded representations than static baselines, achieving competitive or superior performance in over 80% of the datasets, with improvements of up to 135%. The source code is publicly available at https://github.com/UFSCar-LaSID/tai2vec.

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