Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation

What is it about?

In this work, we investigate the utility of Graph Convolutional Networks (GCN) and multi-task learning techniques to capture the tripartite relations between users, items and entities. Based on our study, we propose that in the hybrid structure of the KG, its rich relationships are an essential factor for successful recommendation from both an explanation and performance perspective. We propose a novel method named Light Knowledge Graph Convolutional Network (LKGCN) which explicitly models the high-order connectivities between user items and entities.

Featured Image