Rule-Guided Graph Neural Networks for Recommender Systems

TL;DR

This paper introduces RGRec, a novel recommendation method combining rule learning and graph neural networks to better capture long-range semantics and connectivity in knowledge graphs, improving recommendation accuracy especially in cold start scenarios.

Contribution

It proposes a new approach that integrates rule learning with GNNs for recommender systems, explicitly modeling long-range semantics and entity connectivity.

Findings

Significant improvement over baseline methods on three datasets.

Effective modeling of long-range semantics via learned rules.

Enhanced encoding of entity connectivity improves recommendations.

Abstract

To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items. In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation. RGRec first maps items to corresponding entities in KGs and adds users as new entities. Then, it automatically learns rules to model the explicit long-range semantics, and captures the connectivity between entities by aggregation to better encode various information. We show the effectiveness of RGRec on three real-world datasets. Particularly, the combination of rule learning and GNNs achieves substantial improvement compared to methods only using either of them.