Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning

TL;DR

This paper introduces a novel multi-channel recommender system leveraging hypergraph convolution and contrastive learning, focusing on interactive cross-channel modeling and hierarchical self-supervised learning to enhance recommendation accuracy.

Contribution

It proposes a cross-channel matching representation model and a hierarchical self-supervised learning framework, pioneering their application in recommender systems for improved multi-channel information utilization.

Findings

Significant performance improvements over state-of-the-art methods.

Effective modeling of cross-channel relationships enhances recommendation quality.

Validated benefits of proposed models in general and cold-start scenarios.

Abstract

Recently, leveraging different channels to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be a very promising work. However, how to deeply dig out the relationship between different channels and make full use of it while maintaining the uniqueness of each channel is a problem that has not been well studied and resolved in this field. Under such circumstances, this paper explores and verifies the deficiency of directly constructing contrastive learning tasks on different channels with practical experiments and proposes the scheme of interactive modeling and matching representation across different channels. This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information. To solve this problem, we propose a cross-channel matching representation model based on attentive interaction, which realizes efficient modeling of the relationship between cross-channel information. Based on this, we also propose a hierarchical self-supervised learning model, which realizes two levels of self-supervised learning within and between channels, which improves the ability of self-supervised tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and various metrics on multiple public datasets show that the method proposed in this paper has a significant improvement compared with the state-of-the-art methods, no matter in the general or cold-start scenario. And in the experiment of model variant analysis, the benefits of the cross-channel matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.