Embedding ranking-oriented recommender system graphs

Graph-based recommender systems (GRSs) analyze the structural information available in the graphical representation of data to make better recommendations, especially when direct user-item relation data is sparse. Ranking-oriented GRSs mostly use graphical representation of preference (or rank) data for measuring node similarities, from which they can infer recommendations using neighborhood-based methods. In this paper, we propose PGRec, a novel model-based ranking-oriented recommendation framework. Unlike many other graphbased methods, PGRec extracts vector representations for users and preferences from a novel graph structure called PrefGraph, which models entity relations, feedbacks, and content. A general graph-embedding process is improved and applied to extract vector representations for entities. The resulting embeddings are then used for predicting the target user's unknown pairwise preferences by a neural network based on which a recommendation list is generated for the target user. We have evaluated the proposed method's performance against the state of the art model-based and neighborhood-based recommendation algorithms. Our experiments show that PGRec outperforms the baseline algorithms in terms of the NDCG metric in several datasets.