TBTF: an effective time-varying bias tensor factorization algorithm for recommender system

Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ratings. Tensor factorization is an effective algorithm in context-aware recommendation and current approaches show that adding bias to the tensor factorization model can improve the accuracy. However, users’ rating preferences fluctuate greatly over time, which makes bias fluctuate with time too. Current context-aware recommendation algorithms ignore this problem, and usually use the same bias for a user or an item in different time. Aiming at this problem, this paper first considers the time-varying effect on user bias and item bias in context-aware recommendation, and proposes a time-varying bias tensor factorization recommendation algorithm based on the bias tensor factorization model (BiasTF). We experiment on two real datasets, and the experimental results show that the proposed algorithms get better accuracy than other algorithms.