Meta-graph Embedding in Heterogeneous Information Network for Top-N Recommendation

Heterogeneous Information Network (HIN) is a graph that contains variety of nodes and their relationships. It can provide abundant auxiliary information for the feature engineering of the recommendation model and thus help to improve its recommendation performance. Most work applying the auxiliary information is to calculate node similarities over meta-paths or meta-graphs of HIN and then recommend based on those similarities through matrix factorization or other analogous recommendation algorithms. In this paper, we propose a novel meta-graph embedding based deep learning recommendation model, MGRec. Types of meta-graphs of HIN are embedded as input features through multiple same structured Attention-enhanced CNNs, which help to learn the weight of each node and get a more accurate vector representation of the meta-graph. Besides, a Wide&Multi-Deep structured recommendation framework is designed to learn both the shallow and deep interactions among features, in which multiple independent deep modules are used to learn the distinguishable correlation degree of each type of meta-graph to the target user and item to highlight the distinguishable contribution of each meta-graph to the recommendation. Experiments on two real-world datasets show that, compared with other popular recommendation models, our MGRec model achieves the best performance in multiple evaluation metrics.