User trends modeling for a content-based recommender system

Recommender systems have been developed to overcome the information overload problem by retrieving the most relevant resources. Constructing an appropriate model to estimate the user interests is the major task of recommender systems. The profile matching and latent factors are two main approaches for user modeling. Although a notion of timestamps has already been applied to address the temporary nature of recommender systems, the evolutionary behavior of such systems is less studied. In this paper, we introduce the concept of trend to capture the interests of user in selecting items among different group of similar items. The trend based user model is constructed by incorporating user profile into a new extension of Distance Dependent Chines Restaurant Process (dd-CRP). dd-CRP which is a Bayesian Nonparametric model, provides a framework for constructing an evolutionary user model that captures the dynamics of user interests. We evaluate the proposed method using a real-world data-set that contains news tweets of three news agencies (New York Times, BBC and Associated Press). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach, and its ability to effectively evolve over time.