A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information

TL;DR

This paper introduces a lightweight heterogeneous graph collaborative filtering model that leverages textual information and advanced NLP models to improve recommendation accuracy, especially in data-sparse scenarios.

Contribution

It proposes a novel RGCN-based method that incorporates pre-trained NLP embeddings and a neural matching function, enhancing recommendation performance over existing methods.

Findings

Significant performance improvements on three datasets

Effective alleviation of data sparsity issues

Superior to several baseline models

Abstract

Due to the development of graph neural networks, graph-based representation learning methods have made great progress in recommender systems. However, data sparsity is still a challenging problem that most graph-based recommendation methods are confronted with. Recent works try to address this problem by utilizing side information. In this paper, we exploit the relevant and easily accessible textual information by advanced natural language processing (NLP) models and propose a light RGCN-based (RGCN, relational graph convolutional network) collaborative filtering method on heterogeneous graphs. Specifically, to incorporate rich textual knowledge, we utilize a pre-trained NLP model to initialize the embeddings of text nodes. Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity. Moreover, the matching function used by most graph-based representation learning methods is the inner product, which is not appropriate for the obtained embeddings that contain complex semantics. We design a predictive network that combines graph-based representation learning with neural matching function learning, and demonstrate that this architecture can bring a significant performance improvement. Extensive experiments are conducted on three publicly available datasets, and the results verify the superior performance of our method over several baselines.