ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

TL;DR

This paper introduces ATBRG, a novel graph neural network framework that adaptively models the structural relations between users and items over knowledge graphs, significantly improving recommendation accuracy.

Contribution

The paper proposes the ATBRG framework with graph connect and prune techniques, relation-aware extractor, and activation layers to better capture user-item relations in knowledge graphs.

Findings

ATBRG outperforms state-of-the-art methods on benchmark datasets.

ATBRG achieves a 5.1% CTR improvement in Taobao deployment.

The approach effectively models structural relations in knowledge graphs.

Abstract

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.