A Temporal and Topic-Aware Recommender Model

Individuals' interests and concerning topics are generally changing over time, with a strong impact on their behaviors in social media. Accordingly, designing an intelligent recommender system which can adapt with the temporal characters of both factors becomes a significant research task. In this paper, we suppose that users' current interests and topics are transferred from the previous time step with a Markov property. Based on this idea, we focus on designing a dynamic recommender model based on collective factorization, named Temporal and Topic-Aware Recommender Model (TTARM), which can express the transition process of both user interests and relevant topics in fine granularity. It is a hybrid recommender model which joint Collaborative Filtering (CF) and Content-based recommender method, thus can produce promising recommendations about both existing and newly published items. Experimental results on two real-life data sets from CiteULike and MovieLens demonstrate the effectiveness of our proposed model.