Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks

Abstract

This paper focuses on the precise recommendation of scientific papers in academic networks where users’ social structure, items’ content and attributes exist and have to be profoundly exploited. Different from conventional collaborative filtering cases with only a user-item utility matrix, we study the standard latent factor model and extend it to a heterogeneous one, which models the interaction of different kinds of information. This latent model is called “Content + Attributes”, which incorporates latent topics and descriptive attributes using probabilistic matrix factorization and topic modeling to figure out the final recommendation results in heterogeneous scenarios. Moreover, we further propose a solution to handle the cold start problem of new users by adopting social structures. We conduct extensive experiments on the DBLP dataset and the experimental results show that our proposed model outperforms the baseline methods.