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Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit(More)
An incisive understanding of human lifestyles is not only essential to many scientific disciplines, but also has a profound business impact for targeted marketing. In this paper, we present <b>LifeSpec</b>, a <i>computational framework</i> for exploring and hierarchically categorizing urban lifestyles. Specifically, we have developed an algorithm to connect(More)
An incisive understanding of personal psychological traits is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior. In this paper, we focus on understanding individual novelty-seeking trait(More)
Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to business and governments. Recent studies suggest that human mobility is highly regular and predictable. Additionally, social conformity theory indicates that people's movements are influenced by others. However, existing approaches(More)
With the growing popularity of location-based social networks , vast amount of user check-in histories have been accumulated. Based on such historical data, predicting a user's next check-in place is of much interest recently. There is, however, little study on the limit of predictability of this task and its correlation with users' demographics. These(More)
With the increasing popularity of Location-based Social Networks, a vast amount of location check-ins have been accumulated. Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed. To this end, in this paper, we leveraged collaborative filtering techniques(More)
Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid collection of information on the web, the knowledge base provides heterogeneous information including(More)