{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:03:29Z","timestamp":1753887809505,"version":"3.41.2"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":94,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["364 2019-MS-111"],"award-info":[{"award-number":["364 2019-MS-111"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872069"],"award-info":[{"award-number":["61872069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the popularity of location\u2010based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial\u2010temporal context information effectively to mine the user\u2019s mobility pattern and predict the users\u2019 next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial\u2010temporal self\u2010attention network), which can integrate spatial\u2010temporal information with the self\u2010attention for location prediction. In STSAN, we design a trajectory attention module to learn users\u2019 dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self\u2010attention; spatial attention, which captures user\u2019s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real\u2010world check\u2010ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial\u2010temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.<\/jats:p>","DOI":"10.1155\/2021\/6692313","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T18:20:18Z","timestamp":1617646818000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Spatial\u2010Temporal Self\u2010Attention Network (STSAN) for Location Prediction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1533-1051","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0104-8239","authenticated-orcid":false,"given":"AnLiang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0454-3761","authenticated-orcid":false,"given":"Shuai","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-2792","authenticated-orcid":false,"given":"WenZhu","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1323-344X","authenticated-orcid":false,"given":"BoWei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7327-0955","authenticated-orcid":false,"given":"Shuai","family":"Yao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7365-0835","authenticated-orcid":false,"given":"Muhammad","family":"Asif","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"AsaharaA. 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