Distinguishing latent interaction types from implicit feedbacks for recommendation

Abstract

For privacy considerations, many recommendation algorithms are only based on a kind of implicit feedbacks, where various user behaviors, like view, purchase and etc., are simplified as binary interactions. As side information is assumed not available, these algorithms mainly focus on how to encode users and items via a bipartite graph (viz. the user-item interaction matrix), while ignoring to encode edges for distinguishing the interaction types. In this paper, we argue that implicit feedbacks can be classified into a few user-item relations (viz., latent interaction types) via encoding the edges of a bipartite graph. In particular, we design an edge distinguishment module (EDM) into our neural recommendation model, called Relation-Aware Neural Model (RANM). Based on the latent interaction types, we divide the bipartite graph into a few subgraphs, each consisting of only edges of the same relation and their connected user and item nodes. We propose a Relation-Aware Graph Neural Network (RAGNN) for learning user and item representations. For encoding items, we apply the RAGNN on the relation-aware bipartite graph; While for encoding a user, we first encode several latent interests each on one subgraph and then fuse these interest encodings as the user representation. Experiments on three public datasets validate that our approach of edge classification and representation learning help improving recommendation performance compared with the state-of-the-art competitors. The implementations are available at https://github.com/lulu0913/RAGNN.