Collaborative Filtering

Collaborative filtering (CF) is a core technique in recommender systems aiming to predict user preferences by leveraging past user-item interactions. Current research emphasizes improving CF's accuracy and efficiency, particularly addressing challenges like cold-start problems (limited user data) and biases (favoring popular items or user groups), through advancements in model architectures such as graph neural networks, diffusion models, and autoencoders, as well as refined loss functions and sampling strategies. These improvements are crucial for enhancing the personalization and fairness of recommendation systems across diverse applications, from e-commerce and social media to specialized domains like course selection and real estate.

Papers

November 12, 2023

Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Yijie Zhang, Yuanchen Bei, Shiqi Yang, Hao Chen, Zhiqing Li, Lijia Chen, Feiran Huang
Collaborative Filtering Recommendation Model Recommendation Performance Data Imbalance Multi Behavior

November 3, 2023

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces
Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava
Recommendation System Collaborative Filtering Recommendation Model Federated Averaging Heterogeneous Setting Neural Collaborative Filtering Random Subspace

November 1, 2023

A Collaborative Filtering-Based Two Stage Model with Item Dependency for Course Recommendation
Eric L. Lee, Tsung-Ting Kuo, Shou-De Lin
Recommender System Graph Based Collaborative Filtering Dependency Aware Incident Stage Model Course Recommendation Course Recommender System

October 31, 2023

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints
Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain
Multi Armed Bandit Collaborative Filtering Sub Linear Regret Budget Constraint Regret Optimal Algorithm Latent Bandit Collaborative Bandit

October 22, 2023

Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering
Ruilin Yuan, Leya Li, Yuanzhe Cai
Collaborative Filtering Effective Approach Co Occurrence Graph Novel Zero Shot Item2Vec Framework

October 15, 2023

When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
David Liu, Jackie Baek, Tina Eliassi-Rad
Principal Component Analysis Movie Recommendation Collaborative Filtering Latent Feature Fair Principal Component Analysis

October 12, 2023

Metrics for popularity bias in dynamic recommender systems
Valentijn Braun, Debarati Bhaumik, Diptish Dey
Recommender System Absolute Stance Bias Metric Library Collaborative Filtering Popularity Bias Dynamic Recommender System

September 4, 2023

Interactive Graph Convolutional Filtering
Jin Zhang, Defu Lian, Hong Xie, Yawen Li, Enhong Chen
Multi Armed Bandit Collaborative Filtering Interactive Recommender System

August 28, 2023

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang, Chaozhuo Li, Senzhang Wang, Feiran Huang, Sunghun Kim
Graph Neural Network Transformer Megatron Decepticons Recommender System Collaborative Filtering GNN Based

August 14, 2023

MM-GEF: Multi-modal representation meet collaborative filtering
Hao Wu, Alejandro Ariza-Casabona, Bartłomiej Twardowski, Tri Kurniawan Wijaya
Multi Modal Collaborative Filtering Latent Structure Item Response Theory Multi Modal Representation Item Representation

August 11, 2023

Toward a Better Understanding of Loss Functions for Collaborative Filtering
Seongmin Park, Mincheol Yoon, Jae-woong Lee, Hogun Park, Jongwuk Lee
Loss Function Collaborative Filtering Deep Understanding Popularity Bias Modern Recommender System

August 3, 2023

August 1, 2023

July 12, 2023

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li
Machine Learning High Efficiency Collaborative Filtering Joint Learning Architecture Search

June 28, 2023

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu
Collaborative Filtering Negative Sampling Negative Sample Neighbor Sampling

June 22, 2023

Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
Shamal Shaikh, Venkateswara Rao Kagita, Vikas Kumar, Arun K Pujari
Data Augmentation Recommender System Semi Supervised Matrix Factorization Collaborative Filtering Rating Prediction Near Optimal Virtual Rating

June 8, 2023

Safe Collaborative Filtering
Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura
Large Scale Collaborative Filtering Recommendation Quality General Tail Behavior

June 6, 2023

On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Jiayan Guo, Lun Du, Xu Chen, Xiaojun Ma, Qiang Fu, Shi Han, Dongmei Zhang, Yan Zhang
Recommender System Collaborative Filtering Graph Filter User Item Graph Collaborative Filtering Based Collaborative Filtering

Collaborative Filtering

Papers

Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces

A Collaborative Filtering-Based Two Stage Model with Item Dependency for Course Recommendation

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints

Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering

When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations

Metrics for popularity bias in dynamic recommender systems

Interactive Graph Convolutional Filtering

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

MM-GEF: Multi-modal representation meet collaborative filtering

Toward a Better Understanding of Loss Functions for Collaborative Filtering

Incorporating Recklessness to Collaborative Filtering based Recommender Systems

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction

Collaborative filtering to capture AI user's preferences as norms

Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization

Safe Collaborative Filtering

On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering