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Eng."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users\u2019 next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial\u2013temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users\u2019 check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26\u20136.55<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and MAP@k by 3.49\u20136.55<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>%<\/mml:mo>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> on the Hit@k. The code can be downloaded at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Rih0\/sstp\">https:\/\/github.com\/Rih0\/sstp<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s41019-023-00221-y","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:02:54Z","timestamp":1693389774000},"page":"329-343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation"],"prefix":"10.1007","volume":"8","author":[{"given":"Junzhuang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1846-4941","authenticated-orcid":false,"given":"Yuhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixiong","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"221_CR1","doi-asserted-by":"crossref","unstructured":"Werneck H, Silva N, Viana MC, Mour\u00e3o F, Pereira AC, Rocha L (2020) A survey on point-of-interest recommendation in location-based social networks. 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