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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>Human mobility annotation aims to assign mobility records the corresponding visiting Point-of-Interests (POIs). It is one of the most fundamental problems for understanding human mobile behaviors. In literature, many efforts have been devoted to annotating mobility records in a pointwise or trajectory-wise manner. However, the user preference factor is not fully explored and, worse still, the mobile peer influence factor has never been integrated. To this end, in this article, we propose a novel framework, named JEPPI, to jointly exploit user preference and mobile peer influence to tackle the problem. In our JEPPI, we first unify the two distinct factors in a behavior-driven user-POI graph. This graph enables us to model user preference with user-POI visiting relationships, and model two types of mobile peer influence with co-location and co-visiting peer relationships, respectively. Moreover, we devise an equivalence-emphasizing metric to reduce redundancy in the second-order co-visiting peer influence. In addition, a mutual augmentation learning approach is proposed to preserve the latent structures of various factors exploited. Notably, our learning approach preserves all factors in a shared representation space such that user preference is learned with mobile peer influence being considered at the same time, and vice versa. In this way, the different factors are mutually augmented and semantically integrated to enhance human mobility annotation. Finally, using two large-scale real-world datasets, we conduct extensive experiments to demonstrate the superiority of our approach compared with the state-of-the-art annotation methods.<\/jats:p>","DOI":"10.1145\/3406600","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T04:10:30Z","timestamp":1601352630000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploiting User Preference and Mobile Peer Influence for Human Mobility Annotation"],"prefix":"10.1145","volume":"14","author":[{"given":"Renjun","family":"Hu","sequence":"first","affiliation":[{"name":"Beihang University 8 Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing, China"}]},{"given":"Yanchi","family":"Liu","sequence":"additional","affiliation":[{"name":"NEC Labs, Princeton, NJ"}]},{"given":"Yanyan","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China"}]},{"given":"Jingbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China"}]},{"given":"Shuai","family":"Ma","sequence":"additional","affiliation":[{"name":"Beihang University 8 Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing, China"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Rutgers University, Newark, NJ"}]}],"member":"320","published-online":{"date-parts":[[2020,9,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341012.1341041"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2019.2938239"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2849727"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2799679"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3182164"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498256"},{"volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201919)","year":"2019","author":"Hu Renjun","key":"e_1_2_1_9_1"},{"volume-title":"NCF: A neural context fusion approach to raw mobility annotation","year":"2020","author":"Hu Renjun","key":"e_1_2_1_10_1"},{"volume-title":"Can multiple social ties help improve human location prediction? 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