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Internet Technol."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>\n            The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user\u2019s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What\u2019s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts (\n            <jats:italic>DPSR<\/jats:italic>\n            for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What\u2019s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of\n            <jats:italic>DPSR<\/jats:italic>\n            , we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.\n          <\/jats:p>","DOI":"10.1145\/3583687","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T12:48:43Z","timestamp":1676292523000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-6818","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"first","affiliation":[{"name":"Hunan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-7454","authenticated-orcid":false,"given":"Wenjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hunan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8648-141X","authenticated-orcid":false,"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"Temple University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-7716","authenticated-orcid":false,"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"State University of New York, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1055709.1055714"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240410"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/3114179.3114334"},{"key":"e_1_3_2_5_2","first-page":"3301","volume-title":"IJCAI","author":"Chang Buru","year":"2018","unstructured":"Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, and Jaewoo Kang. 2018. Content-Aware hierarchical Point-of-Interest embedding model for successive POI recommendation. 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