Combining Meta-path Instances into Layer-Wise Graphs for Recommendation

In the recommendation area, the concept of meta-path is famous for inferring explicit and effective relationships between nodes such as users and items. To extract useful information from the instances of meta-paths, existing methods embed meta-path instances separately. However, they ignore the complicated semantics presented by multiple instances. These complicated semantics not only provide additional information but also affect the semantics of single instances. Without considering the complicated semantics, the information extracted from the instances may be incomplete and less effective. To solve the problem, we propose to learn the complicated semantics by combining meta-path instances into layer-wise graphs (instance-graphs) for recommendation. Following the idea, we develop an Instance-Graph based Recommendation method (IGR). IGR combines meta-path instances into layer-wise instance-graphs. Then, the instance-graphs are investigated layer by layer to generate effective embeddings. Finally, these embeddings are discriminatively merged into user/item embeddings to make predictions. Extensive experimental results show that IGR outperforms various state-of-the-arts recommendation methods.