实验室2019届硕士研究生杨环同学的论文“Exploring The Interaction Effects for Temporal Spatial Behavior Prediction”（作者：杨环、刘天元、孙宇清、Elisa Bertino）被CIKM2019会议录用。

CIKM（The Conference on Information and Knowledge Management）是数据库、数据挖掘、内容检索方向的旗舰会议，主题是明确未来知识与信息系统发展将面临的挑战和问题，并通过征求和审查高质量，应用和理论研究成果来确定未来的研究方向。CIKM2019将于2019年11月在中国北京举行。

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.