Co-occurrence prediction in a large location-based social network

Rong-Hua Li, Jianquan Liu, Jeffrey Xu Yu, Hanxiong Chen, Hiroyuki Kitagawa
<span title="">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mea4bx5mifbxdbgjmmfhl6qbhq" style="color: black;">Frontiers of Computer Science</a> </i> &nbsp;
Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to "check-in" the places (locations) when they visit them. The accurate geographical and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the
more &raquo; ... spatio-temporal cooccurrences and social ties, and the results show that the cooccurrences are strongly correlative with the social ties. Second, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first introduce two new concepts, bag-of-location and bag-of-time-lag, to characterize user's check-in habits. Based on such bag representations, we define a similarity metric called habits similarity to measure the similarity between two users' check-in habits. Then we propose a machine learning formula for predicting co-occurrence based on the social ties and habits similarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11704-013-3902-8">doi:10.1007/s11704-013-3902-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nnjda23aebgklpwnzowip2hcw4">fatcat:nnjda23aebgklpwnzowip2hcw4</a> </span>
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