A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

Recommender systems are challenging research problems being exploited to suggest new items or services, such as books, music and movies, and even people, to users based on information about the user profile or the recommended items. To date, collaborative filtering (CF) has become one of the most widely used approaches for recommendations. However, traditional CF methods usually cannot track temporal dynamic user preferences and topic changes to make appropriate suggestions. Moreover, the performance of CF is limited in the case of sparse data. In this paper, we propose a novel temporal recommender system based on multiple transitions in user preference drift, called MTUPD, which employs a multitransition factor and a forgetting time function to investigate the evolution of user preferences. In addition, we consider addressing the rating sparsity issue by using text reviews. Understanding the reviews can facilitate the system grasping whether or not a user is attracted by the appearance of an item and whether the facet of an item’s appearance contributes the most to its ratings. To achieve this, we apply a topic model that automatically classifies hidden topic factors in each time period and incorporate the transition method for both user preferences and relevant review topics. Experiments show that our proposed model outperforms the compared models on eight promising datasets for temporal recommender systems.