{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:22:59Z","timestamp":1783574579916,"version":"3.55.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"9","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF China","award":["42050105, U20A20181, U21A20519"],"award-info":[{"award-number":["42050105, U20A20181, U21A20519"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>\n            Recent years have witnessed a vastly increasing popularity of location-based social networks (LBSNs), which facilitates studies on the\n            <jats:italic>next Point-of-Interest (POI) recommendation<\/jats:italic>\n            problem. A user\u2019s POI visiting behavior shows the\n            <jats:italic>sequential transition<\/jats:italic>\n            correlation with previous successive check-ins and the\n            <jats:italic>global spatial-temporal<\/jats:italic>\n            correlation with those check-ins that happened a long time ago at a similar time of day and in geographically close areas. Although previous POI recommendation methods attempted to capture these two correlations, several limitations remain to be solved: (1) RNNs are widely adopted to capture the sequential transition correlation, whereas training an RNN is rather time-consuming given the long input check-in sequence. (2) The pairwise\n            <jats:italic>proximities<\/jats:italic>\n            on time of day and geographical area of check-ins are crucial for global spatial-temporal correlation learning, but have not been comprehensively considered by previous methods. To tackle these issues, we propose a novel next POI recommendation framework named STA-TCN. Specifically, instead of RNNs, STA-TCN augments the Temporal Convolutional Network with gated input injection to learn sequential transition correlation. Furthermore, STA-TCN fuses two novel\n            <jats:italic>grid-difference<\/jats:italic>\n            and\n            <jats:italic>time-sensitivity<\/jats:italic>\n            learning mechanisms with attention network to learn the pairwise spatial-temporal proximities among a user\u2019s check-ins. Extensive experiments are conducted on two large-scale real-world LBSN datasets, and the results show that STA-TCN outperforms the best state-of-the-art baseline with an average improvement of 9.71% and 7.88% on hit rate and normalized discounted cumulative gain, respectively.\n          <\/jats:p>","DOI":"10.1145\/3596497","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T12:17:32Z","timestamp":1683721052000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest Recommendation"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2110-4200","authenticated-orcid":false,"given":"Junjie","family":"Ou","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5178-7198","authenticated-orcid":false,"given":"Haiming","family":"Jin","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0817-7383","authenticated-orcid":false,"given":"Xiaocheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4158-5171","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0357-8356","authenticated-orcid":false,"given":"Xinbing","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3331-2302","authenticated-orcid":false,"given":"Chenghu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai, J. 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