{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T09:36:02Z","timestamp":1761989762766,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"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":["61772249"],"award-info":[{"award-number":["61772249"]}],"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":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The next Point of Interest (<jats:bold>POI<\/jats:bold>) recommendation is the core technology of smart city. Current state-of-the-art models attempt to improve the accuracy of the next POI recommendation by incorporating temporal and spatial intervals or by partitioning the POI coordinates into grids. However, they all overlook a detail that in real life, people always want to know where to go at an exact time point or after a specific time interval instead of aimlessly asking where to go next. Moreover, due to individual preferences, different users may visit different places at the same timestamp. Therefore, utilizing timestamp queries can enhance the personalized recommendation capability of the model and mitigate overfitting risks. These implies that using timestamp can achieve more precise recommendations. To the best of our knowledge, we are the first to use the next timestamp for next POI recommendation. In particular, we propose a <jats:bold>T<\/jats:bold>ime-<jats:bold>S<\/jats:bold>tamp <jats:bold>C<\/jats:bold>ross <jats:bold>A<\/jats:bold>ttention <jats:bold>N<\/jats:bold>etwork (<jats:bold>TSCAN<\/jats:bold>). TSCAN is a two-layer cross-attention network. The first layer, <jats:bold>T<\/jats:bold>ime <jats:bold>S<\/jats:bold>tamp <jats:bold>C<\/jats:bold>ross <jats:bold>A<\/jats:bold>ttention <jats:bold>B<\/jats:bold>lock (<jats:bold>TSCAB<\/jats:bold>), uses cross-attention between the next timestamp and historical timestamps, and multiplies the attention scores on corresponding POI to predict the next POI that is most related to the history. The other layer, <jats:bold>C<\/jats:bold>ross <jats:bold>T<\/jats:bold>ime <jats:bold>I<\/jats:bold>nterval <jats:bold>A<\/jats:bold>ware <jats:bold>B<\/jats:bold>lock (<jats:bold>CTIAB<\/jats:bold>), applies the time intervals between the next timestamp and historical timestamps to the POI obtained by TSCAB and historical POIs, allowing temporally adjacent POIs to have a greater similarity. Our model not only has a significant improvement in accuracy but also achieves the goal of personalized recommendation, effectively alleviating overfitting. We evaluate the proposed model with three real-world LBSN datasets, and show that TSCAN outperforms the state-of-the-art next POI recommendation models by 5~9%. TSCAN can not only recommend the next POI, but also recommend the possible POI to visit at any specific timestamp in the future.<\/jats:p>","DOI":"10.1007\/s41019-023-00240-9","type":"journal-article","created":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T10:02:07Z","timestamp":1706436127000},"page":"88-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Where To Go at the Next Timestamp"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4178-5698","authenticated-orcid":false,"given":"Jiaqi","family":"Duan","sequence":"first","affiliation":[]},{"given":"Xiangfu","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Guihong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,28]]},"reference":[{"key":"240_CR1","unstructured":"Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the twenty-third international joint conference on artificial intelligence. IJCAI \u201913, pp 2605\u20132611. AAAI Press, Beijing"},{"key":"240_CR2","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on world wide web. WWW \u201910, pp 811\u2013820. Association for Computing Machinery, New York. https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"issue":"2","key":"240_CR3","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 Comput Sci 14(2):314\u2013333. https:\/\/doi.org\/10.1007\/s11704-018-8011-2","journal-title":"Front Comput Sci"},{"key":"240_CR4","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710. https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"240_CR5","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K (2018) Personalized Top-N sequential recommendation via convolutional sequence embedding","DOI":"10.1145\/3159652.3159656"},{"key":"240_CR6","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks"},{"key":"240_CR7","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need"},{"key":"240_CR8","doi-asserted-by":"publisher","unstructured":"Kang W-C, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp 197\u2013206. IEEE, Singapore. https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"key":"240_CR9","doi-asserted-by":"publisher","unstructured":"Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2009\u20132019. ACM, Virtual Event. https:\/\/doi.org\/10.1145\/3394486.3403252","DOI":"10.1145\/3394486.3403252"},{"key":"240_CR10","doi-asserted-by":"publisher","unstructured":"Luo Y, Liu Q, Liu Z (2021) Stan: Spatio-temporal attention network for next location recommendation. In: Proceedings of the web conference 2021, pp 2177\u20132185. https:\/\/doi.org\/10.1145\/3442381.3449998","DOI":"10.1145\/3442381.3449998"},{"key":"240_CR11","doi-asserted-by":"publisher","unstructured":"Wang E, Jiang Y, Xu Y, Wang L, Yang Y (2022) Spatial-temporal interval aware sequential poi recommendation. In: 2022 IEEE 38th international conference on data engineering (ICDE), pp 2086\u20132098. IEEE, Kuala Lumpur. https:\/\/doi.org\/10.1109\/ICDE53745.2022.00202","DOI":"10.1109\/ICDE53745.2022.00202"},{"key":"240_CR12","unstructured":"Guo Q, Qi J (2020) SANST: a self-attentive network for next point-of-interest recommendation"},{"key":"240_CR13","doi-asserted-by":"publisher","unstructured":"Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 world wide web conference on world wide web - WWW \u201918, pp 1459\u20131468. ACM Press, Lyon, France. https:\/\/doi.org\/10.1145\/3178876.3186058","DOI":"10.1145\/3178876.3186058"},{"key":"240_CR14","doi-asserted-by":"publisher","unstructured":"Yang D, Fankhauser B, Rosso P, Cudre-Mauroux P (2020) Location prediction over sparse user mobility traces using RNNS: flashback in hidden states! In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, pp 2184\u20132190. International joint conferences on artificial intelligence organization, Yokohama, Japan. https:\/\/doi.org\/10.24963\/ijcai.2020\/302","DOI":"10.24963\/ijcai.2020\/302"},{"key":"240_CR15","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale"},{"key":"240_CR16","doi-asserted-by":"crossref","unstructured":"Zhou G, Song C, Zhu X, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction","DOI":"10.1145\/3219819.3219823"},{"key":"240_CR17","doi-asserted-by":"crossref","unstructured":"Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2018) Deep interest evolution network for click-through rate prediction","DOI":"10.1145\/3219819.3219823"},{"key":"240_CR18","doi-asserted-by":"crossref","unstructured":"Chen Q, Zhao H, Li W, Huang P, Ou W (2019) Behavior sequence transformer for E-commerce recommendation in Alibaba","DOI":"10.1145\/3326937.3341261"},{"key":"240_CR19","doi-asserted-by":"publisher","unstructured":"Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1441\u20131450. ACM, Beijing China. https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"240_CR20","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding"},{"key":"240_CR21","doi-asserted-by":"publisher","unstructured":"Fan X, Liu Z, Lian J, Zhao WX, Xie X, Wen J-R (2021) Lighter and better: low-rank decomposed self-attention networks for next-item recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 1733\u20131737. ACM, Virtual Event Canada. https:\/\/doi.org\/10.1145\/3404835.3462978","DOI":"10.1145\/3404835.3462978"},{"key":"240_CR22","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation","DOI":"10.3115\/v1\/D14-1179"},{"issue":"8","key":"240_CR23","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"240_CR24","doi-asserted-by":"publisher","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI conference on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v30i1.9971","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"240_CR25","doi-asserted-by":"crossref","unstructured":"Zhao P, Zhu H, Liu Y, Xu J, Li Z, Sheng VS, Zhou, X (2019) Where to go next: a spatio-temporal gated network for next poi recommendation. In: AAAI","DOI":"10.1609\/aaai.v33i01.33015877"},{"key":"240_CR26","doi-asserted-by":"publisher","unstructured":"Li J, Wang Y, McAuley J (2020) Time interval aware self-attention for sequential recommendation. In: Proceedings of the 13th international conference on web search and data mining, pp 322\u2013330. ACM, Houston. https:\/\/doi.org\/10.1145\/3336191.3371786","DOI":"10.1145\/3336191.3371786"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-023-00240-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-023-00240-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-023-00240-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T15:02:01Z","timestamp":1711724521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-023-00240-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,28]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["240"],"URL":"https:\/\/doi.org\/10.1007\/s41019-023-00240-9","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"type":"print","value":"2364-1185"},{"type":"electronic","value":"2364-1541"}],"subject":[],"published":{"date-parts":[[2024,1,28]]},"assertion":[{"value":"16 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}