Counterfactual Graph Convolutional Learning for Personalized Recommendation
Authors: 
Meng Jian, Yulong Bai, Xusong Fu, Jingjing Guo, Ge Shi, Lifang WuAuthors Info & Claims
Article No.: 67, Pages 1 - 20
Abstract
Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced interaction clue and produce suboptimal representations to users and items for recommendation. Towards the issue, this work is dedicated to bias-aware embedding learning in a decomposed manner and proposes a counterfactual graph convolutional learning (CGCL) model for personalized recommendation. Instead of debiasing with uniform interaction sampling, we follow the natural interaction bias to model users’ interests with a counterfactual hypothesis. CGCL introduces bias-aware counterfactual masking on interactions to distinguish the effects between majority and minority causes on the counterfactual gap. It forms multiple counterfactual worlds to extract users’ interests in minority causes compared to the factual world. Concretely, users and items are represented with a causal decomposed embedding of majority and minority interests for recommendation. Experiments show that the proposed CGCL is superior to the state-of-the-art baselines. The performance illustrates the rationality of the counterfactual hypothesis in bias-aware embedding learning for personalized recommendation.
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Index Terms
- Counterfactual Graph Convolutional Learning for Personalized Recommendation
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Publication History
Published: 18 June 2024
Online AM: 01 April 2024
Accepted: 12 March 2024
Revised: 06 February 2024
Received: 02 July 2023
Published in TIST Volume 15, Issue 4
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- National Natural Science Foundation of China
- Technical Field Foundation
- Inner Mongolia Autonomous Region Science and Technology Foundation
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