{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:55:13Z","timestamp":1777290913974,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773331"],"award-info":[{"award-number":["61773331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1706218"],"award-info":[{"award-number":["U1706218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41927805"],"award-info":[{"award-number":["41927805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020QF030"],"award-info":[{"award-number":["ZR2020QF030"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2018AAA0100602"],"award-info":[{"award-number":["2018AAA0100602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176243"],"award-info":[{"award-number":["62176243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recommending a limited number of Point-of-Interests (POIs) a user will visit next has become increasingly important to both users and POI holders for Location-Based Social Networks (LBSNs). However, POI recommendation is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recent studies show that embedding techniques effectively incorporate POI contextual information to alleviate the data sparsity issue, and Recurrent Neural Network (RNN) has been successfully employed for sequential prediction. Nevertheless, existing POI recommendation approaches are still limited in capturing user personalized preference due to separate embedding learning or network modeling. To this end, we propose a novel unified spatio-temporal neural network framework, named PPR, which leverages users\u2019 check-in records and social ties to recommend personalized POIs for querying users by joint embedding and sequential modeling. Specifically, PPR first learns user and POI representations by joint modeling User-POI relation, sequential patterns, geographical influence, and social ties in a heterogeneous graph and then models user personalized sequential patterns using the designed spatio-temporal neural network based on LSTM model for the personalized POI recommendation. Furthermore, we extend PPR to an end-to-end recommendation model by jointly learning node representations and modeling user personalized sequential preference. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms state-of-the-art baselines for successive POI recommendation in terms of Accuracy, Precision, Recall and NDCG. The source code is available at:<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.anonymous.4open.science\/r\/DSE-1BEC\">https:\/\/www.anonymous.4open.science\/r\/DSE-1BEC<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s41019-022-00180-w","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T17:03:27Z","timestamp":1643648607000},"page":"44-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation"],"prefix":"10.1007","volume":"7","author":[{"given":"Shaojie","family":"Dai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5924-1410","authenticated-orcid":false,"given":"Yanwei","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"180_CR1","doi-asserted-by":"crossref","unstructured":"Chang B, Park Y, Park D, Kim S, Kang J (2018) Content-aware hierarchical point-of-interest embedding model for successive poi recommendation. In: IJCAI, pp 3301\u20133307","DOI":"10.24963\/ijcai.2018\/458"},{"key":"180_CR2","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247"},{"key":"180_CR3","doi-asserted-by":"crossref","unstructured":"Chen M, Liu Y, Yu X (2014) Nlpmm: a next location predictor with Markov modeling. In: PAKDD. Springer, pp 186\u2013197","DOI":"10.1007\/978-3-319-06605-9_16"},{"key":"180_CR4","unstructured":"Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: IJCAI"},{"key":"180_CR5","doi-asserted-by":"crossref","unstructured":"Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: KDD, pp 1082\u20131090","DOI":"10.1145\/2020408.2020579"},{"key":"180_CR6","doi-asserted-by":"crossref","unstructured":"Dai S, Yu Y, Fan H, Dong J (2021) Personalized poi recommendation: spatio-temporal representation learning with social tie. In: DASFAA (1), pp 558\u2013574","DOI":"10.1007\/978-3-030-73194-6_37"},{"key":"180_CR7","doi-asserted-by":"crossref","unstructured":"Feng S, Cong G, An B, Chee YM (2017) Poi2vec: geographical latent representation for predicting future visitors. In: AAAI, pp 102\u2013108","DOI":"10.1609\/aaai.v31i1.10500"},{"key":"180_CR8","doi-asserted-by":"crossref","unstructured":"Feng S, Tran LV, Cong G, Chen L, Li J, Li F (2020) Hme: a hyperbolic metric embedding approach for next-poi recommendation. In: SIGIR, pp 1429\u20131438","DOI":"10.1145\/3397271.3401049"},{"key":"180_CR9","doi-asserted-by":"crossref","unstructured":"Kim JS, Jin H, Kavak H, Rouly OC, Crooks A, Pfoser D, Wenk C, Z\u00fcfle A (2020) Location-based social network data generation based on patterns of life. In: 2020 21st IEEE international conference on mobile data management (MDM). IEEE, pp 158\u2013167","DOI":"10.1109\/MDM48529.2020.00038"},{"key":"180_CR10","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"180_CR11","doi-asserted-by":"crossref","unstructured":"Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) Lars: a location-aware recommender system. In: ICDE. IEEE, pp 450\u2013461","DOI":"10.1109\/ICDE.2012.54"},{"key":"180_CR12","doi-asserted-by":"crossref","unstructured":"Li K, Lu G, Luo G, Cai Z (2020) Seed-free graph de-anonymiztiation with adversarial learning. In: CIKM, pp 745\u2013754","DOI":"10.1145\/3340531.3411970"},{"key":"180_CR13","doi-asserted-by":"crossref","unstructured":"Li X, Cong G, Li XL, Pham TAN, Krishnaswamy S (2015) Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In: SIGIR, pp 433\u2013442","DOI":"10.1145\/2766462.2767722"},{"issue":"4","key":"180_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3354187","volume":"37","author":"X Li","year":"2019","unstructured":"Li X, Han D, He J, Liao L, Wang M (2019) Next and next new poi recommendation via latent behavior pattern inference. ACM Trans Inf Syst (TOIS) 37(4):1\u201328","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"180_CR15","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"180_CR16","doi-asserted-by":"crossref","unstructured":"Liu Z, Huang C, Yu Y, Fan B, Dong J (2020) Fast attributed multiplex heterogeneous network embedding. In: CIKM, pp 995\u20131004","DOI":"10.1145\/3340531.3411944"},{"key":"180_CR17","doi-asserted-by":"crossref","unstructured":"Liu Z, Huang C, Yu Y, Song P, Fan B, Dong J (2020) Dynamic representation learning for large-scale attributed networks. In: CIKM, pp 1005\u20131014","DOI":"10.1145\/3340531.3411945"},{"issue":"3","key":"180_CR18","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1007\/s11280-018-0599-5","volume":"22","author":"YS Lu","year":"2019","unstructured":"Lu YS, Shih WY, Gau HY, Chung KC, Huang JL (2019) On successive point-of-interest recommendation. World Wide Web 22(3):1151\u20131173","journal-title":"World Wide Web"},{"key":"180_CR19","doi-asserted-by":"crossref","unstructured":"Ma C, Zhang Y, Wang Q, Liu X (2018) Point-of-interest recommendation: exploiting self-attentive autoencoders with neighbor-aware influence. In: CIKM, pp 697\u2013706","DOI":"10.1145\/3269206.3271733"},{"key":"180_CR20","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"180_CR21","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: KDD, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"180_CR22","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: WWW, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"180_CR23","doi-asserted-by":"crossref","unstructured":"Wang Q, Yin H, Chen T, Huang Z, Wang H, Zhao Y, Viet\u00a0Hung NQ (2020) Next point-of-interest recommendation on resource-constrained mobile devices. In: WWW, pp 906\u2013916","DOI":"10.1145\/3366423.3380170"},{"key":"180_CR24","unstructured":"Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861\u20136871. PMLR"},{"key":"180_CR25","doi-asserted-by":"crossref","unstructured":"Wu Y, Li K, Zhao G, Xueming Q (2020) Personalized long-and short-term preference learning for next poi recommendation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2020.3002531"},{"key":"180_CR26","doi-asserted-by":"crossref","unstructured":"Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. In: CIKM, pp 15\u201324","DOI":"10.1145\/2983323.2983711"},{"key":"180_CR27","doi-asserted-by":"crossref","unstructured":"Ye J, Zhu Z, Cheng H (2013) What\u2019s your next move: User activity prediction in location-based social networks. In: SDM. SIAM, pp 171\u2013179","DOI":"10.1137\/1.9781611972832.19"},{"key":"180_CR28","doi-asserted-by":"crossref","unstructured":"Ye M, Yin P, Lee WC (2010) Location recommendation for location-based social networks. In: SIGSPATIAL, pp 458\u2013461","DOI":"10.1145\/1869790.1869861"},{"key":"180_CR29","doi-asserted-by":"crossref","unstructured":"Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: SIGIR, pp 363\u2013372","DOI":"10.1145\/2484028.2484030"},{"key":"180_CR30","doi-asserted-by":"crossref","unstructured":"Zhang JD, Chow CY, Li Y (2014) Lore: exploiting sequential influence for location recommendations. In: SIGSPATIAL, pp 103\u2013112","DOI":"10.1145\/2666310.2666400"},{"issue":"2","key":"180_CR31","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/s11704-018-8011-2","volume":"14","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Li C, Wu Z, Sun A, Ye D, Luo X (2020) Next: a neural network framework for next poi recommendation. Front Comp Sci 14(2):314\u2013333","journal-title":"Front Comp Sci"},{"issue":"3","key":"180_CR32","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1007\/s11280-018-0579-9","volume":"22","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Liu Y, Zhang Z, Shen B (2019) Fused matrix factorization with multi-tag, social and geographical influences for poi recommendation. World Wide Web 22(3):1135\u20131150","journal-title":"World Wide Web"},{"key":"180_CR33","doi-asserted-by":"crossref","unstructured":"Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: IJCAI, pp 3216\u20133222","DOI":"10.24963\/ijcai.2020\/445"},{"key":"180_CR34","doi-asserted-by":"crossref","unstructured":"Zhao P, Luo A, Liu Y, Zhuang F, Xu J, Li Z, Sheng VS, Zhou X (2020) Where to go next: a spatio-temporal gated network for next poi recommendation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2020.3007194"},{"key":"180_CR35","doi-asserted-by":"crossref","unstructured":"Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: WWW, pp 153\u2013162","DOI":"10.1145\/3041021.3054138"},{"key":"180_CR36","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"key":"180_CR37","doi-asserted-by":"crossref","unstructured":"Zhu Y, Li H, Liao Y, Wang B, Guan Z, Liu H, Cai D (2017) What to do next: modeling user behaviors by time-lstm. In: IJCAI, pp 3602\u20133608","DOI":"10.24963\/ijcai.2017\/504"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-022-00180-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-022-00180-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-022-00180-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T09:32:21Z","timestamp":1674639141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-022-00180-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,31]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["180"],"URL":"https:\/\/doi.org\/10.1007\/s41019-022-00180-w","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"value":"2364-1185","type":"print"},{"value":"2364-1541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,31]]},"assertion":[{"value":"29 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interests regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}