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Inf. Syst."],"published-print":{"date-parts":[[2017,10,31]]},"abstract":"<jats:p>Two characteristics of location-based services are mobile trajectories and the ability to facilitate social networking. The recording of trajectory data contributes valuable resources towards understanding users\u2019 geographical movement behaviors. Social networking is possible when users are able to quickly connect to anyone nearby. A social network with location based services is known as location-based social network (LBSN). As shown in Cho et al. [2013], locations that are frequently visited by socially related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. To better analyze and mine LBSN data, we need to have a comprehensive view of each of these two aspects, i.e., the mobile trajectory data and the social network.<\/jats:p>\n          <jats:p>Specifically, we present a novel neural network model that can jointly model both social networks and mobile trajectories. Our model consists of two components: the construction of social networks and the generation of mobile trajectories. First we adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. The key to our model lies in generating mobile trajectories. Second, we consider four factors that influence the generation process of mobile trajectories: user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at the short or long term levels. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. In particular, the improvement over baselines is more significant when either network structure or trajectory data is sparse.<\/jats:p>","DOI":"10.1145\/3041658","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":131,"title":["A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories"],"prefix":"10.1145","volume":"35","author":[{"given":"Cheng","family":"Yang","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maosong","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wayne Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward Y.","family":"Chang","sequence":"additional","affiliation":[{"name":"HTC Research 8 Innovation, Palo Alto, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,8,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2424321.2424348"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/2980539.2980616"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741077"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1625275.1625711"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI\u201912)","volume":"12","author":"Cheng Chen","unstructured":"Chen Cheng , Haiqin Yang , Irwin King , and Michael R. 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