Awesome Collaborative Filtering

Key papers

• MTGR: Industrial-Scale Generative Recommendation Framework in Meituan (2025)
  Ruidong Han et al.
  11.46
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024)
  Yupeng Hou et al.
  11.11
• Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation (2025)
  Han Liu et al.
  9.87
• Towards Explainable Personalized Recommendations by Learning from Users' Photos (2025)
  Jorge D\'iez et al.
  9.39
• A Survey on Multi-Behavior Sequential Recommendation (2023)
  Xiaoqing Chen et al.
  9.14
• Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation (2025)
  Zheqi Lv et al.
  8.81
• Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M (2025)
  Dario Di Palma et al.
  8.50
• Unleashing the Power of Large Language Model for Denoising Recommendation (2025)
  Shuyao Wang et al.
  8.01
• Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation (2024)
  Jinfeng Xu et al.
  7.99
• DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System (2025)
  Wencai Ye et al.
  7.97
• Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems (2024)
  Pan Li et al.
  7.50
• AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language Embeddings (2025)
  Guoqing Hu et al.
  7.46
• A Survey on Generative Recommendation: Data, Model, and Tasks (2025)
  Min Hou et al.
  7.28
• Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation (2025)
  Yi Zhang et al.
  7.13
• LettinGo: Explore User Profile Generation for Recommendation System (2025)
  Lu Wang et al.
  7.06
• ThinkRec: Thinking-based recommendation via LLM (2025)
  Qihang Yu et al.
  7.00
• Reinforced Prompt Personalization for Recommendation with Large Language Models (2024)
  Wenyu Mao et al.
  6.96
• Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks (2025)
  Yushang Zhao et al.
  6.64
• Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation (2025)
  Shengyin Sun and Chen Ma
  6.58
• RecoWorld: Building Simulated Environments for Agentic Recommender Systems (2025)
  Fei Liu et al.
  6.56
• FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation (2025)
  Mingzhe Han et al.
  6.53
• Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation (2025)
  Shijia Wang et al.
  6.51
• S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain (2025)
  Rui Xia et al.
  6.36
• Multi-Behavior Recommender Systems: A Survey (2025)
  Kyungho Kim et al.
  6.23
• Enhancing User Intent for Recommendation Systems via Large Language Models (2025)
  Xiaochuan Xu et al.
  6.12
• Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering (2025)
  Tin T. Tran et al.
  6.12
• Multidimensional Item Response Theory in the Style of Collaborative Filtering (2023)
  Yoav Bergner et al.
  6.08
• Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems (2025)
  Xiao Zhou et al.
  6.07
• Pctx: Tokenizing Personalized Context for Generative Recommendation (2025)
  Qiyong Zhong et al.
  6.04
• Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation (2025)
  Enze Liu et al.
  5.96
• Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights (2023)
  Ruyu Li et al.
  5.91
• Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems (2025)
  Shuqiang Zhang et al.
  5.82
• CARTS: Collaborative Agents for Recommendation Textual Summarization (2025)
  Jiao Chen et al.
  5.82
• Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach (2025)
  Jialei Chen et al.
  5.76
• Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration (2025)
  Hongji Li et al.
  5.70
• Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation (2024)
  Sichun Luo et al.
  5.63
• Federated Recommender System with Data Valuation for E-commerce Platform (2025)
  Jongwon Park et al.
  5.63
• Continual Recommender Systems (2025)
  Hyunsik Yoo et al.
  5.52
• Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation (2026)
  Anh Truong et al.
  5.49
• CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models (2025)
  Junze Chen et al.
  5.46
• Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation (2025)
  Teng Shi et al.
  5.35
• CombiGCN: An effective GCN model for Recommender System (2025)
  Loc Tan Nguyen et al.
  5.29
• Continual Low-Rank Adapters for LLM-based Generative Recommender Systems (2025)
  Hyunsik Yoo et al.
  5.26
• RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning (2025)
  Jian Xu et al.
  5.24
• Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors (2025)
  Himanshu Thakur et al.
  5.21
• An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation (2025)
  Junrui Liu and Tong Li and Di Wu and Zifang Tang and Yuan Fang and Zhen Yang
  5.18
• Request-Only Optimization for Recommendation Systems (2025)
  Liang Guo et al.
  5.15
• DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation (2024)
  Hourun Li et al.
  5.13
• Listwise Preference Alignment Optimization for Tail Item Recommendation (2025)
  Zihao Li et al.
  5.10
• TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback (2022)
  Jie Wang et al.
  5.06
• Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation (2025)
  Jiang Zhang et al.
  5.04
• Capturing User Interests from Data Streams for Continual Sequential Recommendation (2025)
  Gyuseok Lee et al.
  5.04
• Differentiable Fuzzy Neural Networks for Recommender Systems (2025)
  Stephan Bartl et al.
  4.98
• Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025)
  Song Jin et al.
  4.98
• Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits (2025)
  Maoli Liu et al.
  4.98
• COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation (2025)
  Jinfeng Xu et al.
  4.93
• Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation (2025)
  Chenhao Zhai et al.
  4.82
• MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation (2025)
  Xiangjin Xie et al.
  4.82
• The Application of Large Language Models in Recommendation Systems (2025)
  Peiyang Yu et al.
  4.76
• Value Function Decomposition in Markov Recommendation Process (2025)
  Xiaobei Wang et al.
  4.76