Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation

TL;DR

This paper introduces IHGCL, a novel framework that enhances heterogeneous graph contrastive learning for recommendation systems by capturing user intents through meta-paths and reducing noise via a bottleneck autoencoder.

Contribution

The paper proposes a new intent-guided contrastive learning framework that integrates meta-paths and employs a bottleneck autoencoder to improve recommendation accuracy.

Findings

Outperforms baseline methods on six datasets.

Effectively captures user intents via meta-paths.

Reduces noise with a novel autoencoder approach.

Abstract

Contrastive Learning (CL)-based recommender systems have gained prominence in the context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of representations across different views. However, existing frameworks often neglect the fact that user-item interactions within HG are governed by diverse latent intents (e.g., brand preferences or demographic characteristics of item audiences), which are pivotal in capturing fine-grained relations. The exploration of these underlying intents, particularly through the lens of meta-paths in HGs, presents us with two principal challenges: i) How to integrate CL with intents; ii) How to mitigate noise from meta-path-driven intents. To address these challenges, we propose an innovative framework termed Intent-guided Heterogeneous Graph Contrastive Learning (IHGCL), which designed to enhance CL-based recommendation by capturing the intents contained within meta-paths. Specifically, the IHGCL framework includes: i) a meta-path-based Dual Contrastive Learning (DCL) approach to effectively integrate intents into the recommendation, constructing intent-intent contrast and intent-interaction contrast; ii) a Bottlenecked AutoEncoder (BAE) that combines mask propagation with the information bottleneck principle to significantly reduce noise perturbations introduced by meta-paths. Empirical evaluations conducted across six distinct datasets demonstrate the superior performance of our IHGCL framework relative to conventional baseline methods. Our model implementation is available at https://github.com/wangyu0627/IHGCL.