Collaborative Filtering

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Collaborative filtering uses patterns in past user-item interactions to predict what people may like, even when item descriptions are unavailable. Common approaches include matrix factorization, neighborhood methods, and graph neural networks that pass information between users and items; implicit-feedback systems also use ranking objectives and sampling strategies. Newer models combine collaborative signals with text, knowledge graphs, or semantic item representations, and address cold start, popularity bias, fairness, and memory costs. Methods are commonly compared using ranking metrics such as Recall and NDCG, but a central tension is how to gain useful semantic information without weakening strong collaborative signals, especially for sparse or underexposed items.

Latest papers

32 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation
Oct 14, 2026Yuanfeng QiuCold-Start Recommendation Collaborative Filtering
The Hong Kong University of Science and Technology (Guangzhou)
Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)
Oct 1, 2026Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby +3Semantic ID Recommendation Graph Clustering
Huawei Ireland Research Centre Dublin, Ireland
A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations
Sep 4, 2026Syed Mohammed Arshad Zaidi, Eric Rincon, Shayan HassantabarRecommender Systems Collaborative Filtering
Expedia Group, Austin, Texas, USA
When Memory Takes Gradients: Collaborative Vector Memory for Agentic Recommender Systems
Aug 27, 2026Hanchong Chen, Xing Tang, Lingjie Li +2Recommender Systems Large Language Model-Based Recommendation
Shenzhen Technology University
Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
Aug 24, 2026Jiaqi Wang, Tianying Liu, Heng Chang +3Generative Recommendation Diffusion Models
Fudan University, Shanghai, China · Tongji University, Shanghai, China
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
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
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
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
Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering
Jun 19, 2026Jin-Duk Park, Won-Yong ShinMemory-Efficient Inference Krylov Subspace Methods
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul 03722, Republic of Korea
How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
Jun 15, 2026Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura +1Feature Interaction Modeling Recommender Systems
Instituto de Computac¸˜ao – Universidade Federal da Bahia (UFBA) Salvador – BA – Brazil
Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
Jun 4, 2026Anh Truong, John Trenkle, Yuanbo Chen +4Cold-Start Recommendation Graph Representation Learning
Tubi San Francisco, USA · Kumo AI San Francisco, USA
Acting on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation
May 25, 2026Alexander Apartsin, Yigal Meshulam, Yehudit ApersteinRobust Matrix Completion Multi-Robot Task Allocation
Holon Institute of Technology, Holon, Israel. · Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
May 22, 2026Özkan CanaySequential Recommendation Multi-Criteria Decision Analysis
aSakarya University, Department of Information Systems and Technologies, Faculty of Computer and Information Sciences, Sakarya, Türkiye
Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
May 11, 2026Md Aminul Islam, Ahmed Sayeed Faruk, Sourav Medya +1Graph Neural Networks Popularity Bias
University of Illinois Chicago
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
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
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.
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
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
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
ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation
Apr 14, 2026Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai +1Density-Based Clustering Retrieval-Augmented Generation
University of Arkansas, Fayetteville, AR 72701, USA · Walmart Inc., Bentonville, AR 72716, USA
The Unreasonable Effectiveness of Data for Recommender Systems
Apr 7, 2026Youssef AbdouRecommender Systems Collaborative Filtering
A Logical-Rule Autoencoder for Interpretable Recommendations
Apr 5, 2026Jinhao Pan, Bowen Wei, Ziwei ZhuExplainable Recommendation Collaborative Filtering
Department of Computer Science, George Mason University, Fairfax, Virginia, USA
Pseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
Mar 25, 2026Pronob Kumar Barman, Tera L. Reynolds, James FouldsCollaborative Filtering Cold-Start Recommendation
Department of Information Systems, University of Maryland, Baltimore County, Baltimore, Maryland, USA
RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation
Mar 17, 2026Yixuan Huang, Jiawei Chen, Shengfan Zhang +1Graph Contrastive Learning Collaborative Filtering
National University of Defense Technology, Changsha, China · Ant Group, Chongqing, China · Tsinghua University, Beijing, China
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
Feb 26, 2026Jiayi Wu, Zhengyu Wu, Xunkai Li +2Collaborative Filtering User Preference Modeling
Beijing Institute of Technology, Beijing, China
A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
Feb 24, 2026Nikita Baidya, Bidyut Kr. Patra, Ratnakar DashFairness in Recommender Systems Recommender Systems
Pattern Recognition Laboratory, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India. · Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, India.
Learning Preference from Observed Rankings
Feb 18, 2026Yu-Chang Chen, Chen Chian Fuh, Shang En TsaiPreference Modeling User Preference Modeling
National Taiwan University

Oct 14, 2026J/K move · Enter open · S save

Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation

Yuanfeng Qiu

The Hong Kong University of Science and Technology (Guangzhou)

Cold-Start Recommendation Collaborative Filtering Multimodal Recommendation Knowledge Distillation

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Graph Collaborative Filtering models have achieved remarkable success in recommendation systems. However, they inherently suffer from a catastrophic representation collapse in strict cold-start scenarios due to the topological isolation of unobserved items. While integrating multi-modal semantic features offers a potential remedy, directly mapping isolated semantic attributes into a globally topology-driven collaborative space frequently precipitates representation degradation and optimization conflicts. To bridge this fundamental structural divide, we propose DualGCN, an innovative Dual-Branch Graph Knowledge Distillation Framework. Operating under a decoupled two-stage training paradigm, DualGCN first constructs a robust collaborative teacher network where item representations are anchored by a topology-smoothed semantic prior, effectively reshaping the latent manifold. Subsequently, a semantic student branch employs a residual mapping mechanism and a magnitude-agnostic cosine distillation objective to achieve high-fidelity cross-modal alignment without disrupting established collaborative signals. Furthermore, we introduce a dynamic norm reconstruction mechanism to rigorously calibrate the physical scales of novel and warm items during global retrieval. Extensive experiments across three real-world benchmark datasets demonstrate that DualGCN significantly outperforms state-of-the-art baselines in strict cold-start scenarios while comprehensively preserving recommendation accuracy for highly active items.