Personalized Recommendation Meets Your Next Favorite
Qiang Song, Jian Cheng, Ting Yuan, Hanqing Lu · 2015
A comprehensive understanding of user's item selection behavior is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent researches have discovered that user's favorites can be divided into 2 categories: long-term and short-term. User's item selection behavior is a mixed decision of her long and short-term favorites. In this paper, we propose a unified model, namely States Transition pAir-wise Ranking Model (STAR), to address users' favorites mining for sequential-set recommendation. Our method utilizes a transition graph for collaborative filtering that accounts for mining user's short-term favorites, jointed with a generative topic model for expressing user's long-term favorites. Furthermore, a user's specific prior is introduced into our unified model for better modeling personalization. Technically, we develop a pair-wise ranking loss function for parameters learning. Empirically, we measure the effectiveness of our method using two real-world datasets and the results show that our method outperforms state-of-the-art methods.