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Data"],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>\n            As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users\u2019 demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users\u2019 historical preferences and real-time context, a location-aware recommender system called TRACE (\n            <jats:bold>T<\/jats:bold>\n            ravel\n            <jats:bold>R<\/jats:bold>\n            einforcement Recommendations Based on Location-\n            <jats:bold>A<\/jats:bold>\n            ware\n            <jats:bold>C<\/jats:bold>\n            ontext\n            <jats:bold>E<\/jats:bold>\n            xtraction) is proposed. It captures users\u2019 features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users\u2019 preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users\u2019 preference shifts based on real-time location is valuable for improving the recommendation results.\n          <\/jats:p>","DOI":"10.1145\/3487047","type":"journal-article","created":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T20:51:00Z","timestamp":1641675060000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-8451","authenticated-orcid":false,"given":"Zhe","family":"Fu","sequence":"first","affiliation":[{"name":"University of North Carolina, Charlotte, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yu","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Niu","sequence":"additional","affiliation":[{"name":"University of North Carolina, Charlotte, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-014-2236-3"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2015.2509965"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3231933"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEA.2015.7334374"},{"key":"e_1_3_1_6_2","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. 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