PSTIE: Time Information Enhanced Personalized Search

Abstract

Personalized search aims to improve the search quality by re-ranking the candidate document list based on user's historical behavior. Existing approaches focus on modeling the order information of user's search history by sequential methods such as Recurrent Neural Network (RNN). However, these methods usually ignore the fine-grained time information associated with user actions. In fact, the time intervals between queries can help to capture the evolution of query intent and document interest of users. Besides, the time intervals between past actions and current query can reflect the re-finding tendency more accurately than discrete steps in RNN. In this paper, we propose PSTIE, a fine-grained Time Information Enhanced model to construct more accurate user interest representations for Personalized Search. To capture the short-term interest of users, we design time-aware LSTM architectures for modeling the subtle interest evolution of users in continuous time. We further leverage time in calculating the re-finding possibility of users to capture the long-term user interest. We propose two methods to utilize the time-enhanced user interest into personalized ranking. Experiments on two datasets show that PSTIE can effectively improve the ranking quality over state-of-the-art models.