User-based collaborative filtering algorithms generally rely on the users' stationary preferences, yet user preferences in real world are seldom stationary. User preference patterns may have the time-evolving statistical properties in many social contexts. Motivated by this phenomenon, we propose a temporal collaborative filtering approach based on temporal Hierarchical Dirichlet Process tHDP. This approach can capture the density changes on the time-evolving datasets. Experiments on large real world datasets demonstrate the superiority of our proposed approach.
['Liang Chen', 'Peidong Zhu']
Matrix Factorization Approach Based on Temporal Hierarchical Dirichlet Process