Exploring the interaction effects for temporal spatial behavior prediction

Abstract

In location based services, predicting users' temporal-spatial behavior is critical for accurate recommendation. In this paper, we adopt a joint embedding (JointE) model to learn the representations of user, location, and users' action in the same latent space. The functionality of a location is the critical factor influencing different elements of the behavior and is learned by an embedding vector encoding crowd behaviors. A user personalized preference is learned from the user historical behaviors and has two features. One is the combination of action and location, which is learned by maximizing the semantic consistency of the observed behaviors. The other is the periodic preference. Inspired by the notion of periodical temporal rules, we introduce the concept of temporal pattern to describe how often users visit places so as to reduce the high temporal variance of behaviors. A projection matrix is introduced to combine the temporal patterns with location functionality. A user behavior is predicted by the joint probability on behavior elements. We conduct experiments against two representative datasets. The results show that our approach outperforms other approaches.