Cover Image

Debiased Collaborative Filtering with Kernel-Based Causal Balancing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Haoxuan, Zheng, Chunyuan, Xiao, Yanghao, Wu, Peng, Geng, Zhi, Chen, Xu, Cui, Peng
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Debiased Recommendation with Noisy Feedback
by: Li, Haoxuan, et al.
Published: (2024)

Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
by: Li, Haoxuan, et al.
Published: (2024)

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
by: Islam, Md Aminul, et al.
Published: (2026)

Frequency-aware Graph Signal Processing for Collaborative Filtering
by: Xia, Jiafeng, et al.
Published: (2024)

Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation
by: Que, Yue, et al.
Published: (2025)

Safe Collaborative Filtering
by: Togashi, Riku, et al.
Published: (2023)

Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering
by: Ebrat, Danial, et al.
Published: (2025)

L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
by: Jin, Xinzhou, et al.
Published: (2024)

TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
by: Wu, Jiayi, et al.
Published: (2026)

Fair Augmentation for Graph Collaborative Filtering
by: Boratto, Ludovico, et al.
Published: (2024)

Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing
by: Xu, Aobo, et al.
Published: (2024)

MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
by: Li, Yuantong, et al.
Published: (2026)

Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
by: Loveland, Donald, et al.
Published: (2024)

Graph Signal Diffusion Model for Collaborative Filtering
by: Zhu, Yunqin, et al.
Published: (2023)

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
by: Huang, Ling, et al.
Published: (2022)

Debiased Model-based Interactive Recommendation
by: Li, Zijian, et al.
Published: (2024)

How Does Message Passing Improve Collaborative Filtering?
by: Ju, Mingxuan, et al.
Published: (2024)

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
by: Maurera, Fernando Benjamín Pérez, et al.
Published: (2022)

ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
by: Shenbin, Ilya, et al.
Published: (2024)

On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
by: Loveland, Donald, et al.
Published: (2025)

Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
by: Pires, Pedro R., et al.
Published: (2026)

Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
by: Zhang, Shuqiang, et al.
Published: (2025)

Learning to Fast Unrank in Collaborative Filtering Recommendation
by: Zhao, Junpeng, et al.
Published: (2025)

Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA
by: Ding, Xiangyun, et al.
Published: (2020)

Geometric Collaborative Filtering with Convergence
by: Husain, Hisham, et al.
Published: (2024)

UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
by: Pan, Lei, et al.
Published: (2023)

Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
by: Kuang, Haoxuan, et al.
Published: (2024)

CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
by: Park, Jin-Duk, et al.
Published: (2024)

Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation
by: Liu, Emily, et al.
Published: (2025)

Sequential Regression for Continuous Value Prediction using Residual Quantization
by: Cui, Runpeng, et al.
Published: (2026)

SVD-AE: Simple Autoencoders for Collaborative Filtering
by: Hong, Seoyoung, et al.
Published: (2024)

Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
by: Hu, Binbin, et al.
Published: (2024)

Neural Causal Graph Collaborative Filtering
by: Wang, Xiangmeng, et al.
Published: (2023)

Uncovering Causal Drivers of Energy Efficiency for Industrial Process in Foundry via Time-Series Causal Inference
by: Ma, Zhipeng, et al.
Published: (2025)

Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
by: Tu, Yuchun, et al.
Published: (2026)

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
by: Li, Zhiwei, et al.
Published: (2024)

Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
by: Gharahighehi, Alireza, et al.
Published: (2025)

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
by: Xu, Xiaoxiao, et al.
Published: (2025)

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
by: Wang, Shuyao, et al.
Published: (2024)

Learning Filter-Aware Distance Metrics for Nearest Neighbor Search with Multiple Filters
by: Sutradhar, Ananya, et al.
Published: (2025)