Deep hybrid knowledge graph embedding for top-n recommendation

Abstract

In knowledge graph (KG) based recommender systems, path-based methods make recommendations by building user-item graphs and exploiting connectivity patterns between the entities in the graph. To overcome the limitations of traditional meta-path based methods that rely heavily on handcrafted meta-paths, recent deep neural network based methods, such as the Recurrent Knowledge Graph Embedding (RKGE) approach, can automatically mine the connectivity patterns between entities in the KG, thereby improving recommendation performance. However, these methods usually use only one type of neural network to encode path embeddings, which cannot fully extract path features, limiting performance improvement of the recommender system. In this paper, we propose a Deep Hybrid Knowledge Graph Embedding (DHKGE) method for top-N recommendation. DHKGE encodes embeddings of paths between users and items by combining convolutional neural network (CNN) and the long short-term memory (LSTM) network. Furthermore, it uses an attention mechanism to aggregate the encoded path representations and generate a final hidden state vector, which is used to calculate the proximity between the target user and candidate items, thus generating top-N recommendation. Experiments on the MovieLens 100K and Yelp datasets show that DHKGE overall outperforms RKGE and several typical recommendation methods in terms of Precision@N, MRR@N, and NDCG@N.