Description

Community detection on social networks typically aims to cluster users into different communities based on their social links. The increasing popularity of Location-based Social Networks offers the opportunity to augment these social links with spatial information, for detecting location-centric communities that frequently visit similar places. Such location-centric communities are important to companies for their location-based and mobile advertising efforts. Due to research on social network, mobile commerce has received lot of interests. Mining and prediction of users’ mobile commerce behaviors such as their movements and purchase transactions. Location-based sequential event prediction is an interesting problem with many real-world applications. For example, knowing when and where people will use certain kinds of services could enable the development of robust anticipatory systems. A key to this problem is in understanding the nature of the process from which sequential data arises. Usually, human behavior exhibits distinct spatial, temporal, and social patterns. The authors examine three kinds of patterns extracted from sequential purchasing events and propose a novel model that captures contextual dependencies in spatial sequence, customers’ temporal preferences, and social influence via an implicit network. The system we propose a probabilistic predictive model that incorporates spatial, temporal, and social interaction features extracted from purchasing events. The proposed probabilistic model is a combination of three components.