Dual-Channel Multiplex Graph Neural Networks for Recommendation

TL;DR

This paper introduces DCMGNN, a novel graph neural network framework that models multiplex user-item relations and behavior patterns to improve recommendation accuracy, outperforming state-of-the-art methods.

Contribution

The work proposes a dual-channel multiplex GNN that explicitly captures behavior patterns and relation impacts, addressing limitations of previous recommendation models.

Findings

Outperforms state-of-the-art methods by 10.06% in Recall@10

Achieves 12.15% higher NDCG@10 on average

Demonstrates effectiveness on three real-world datasets

Abstract

Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10, respectively.