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Web"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Session-based travel packages recommendation aims to predict users\u2019 next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components:\n            <jats:italic>travel packages encoder<\/jats:italic>\n            ,\n            <jats:italic>users behaviors encoder<\/jats:italic>\n            , and\n            <jats:italic>interaction modeling<\/jats:italic>\n            . Specifically, the\n            <jats:italic>travel packages encoder<\/jats:italic>\n            module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The\n            <jats:italic>users behaviors encoder<\/jats:italic>\n            module is used to encode user\u2019 historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users\u2019 representations by exploiting a gated fusion approach. The\n            <jats:italic>interaction modeling<\/jats:italic>\n            module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.\n          <\/jats:p>","DOI":"10.1145\/3577032","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T12:29:35Z","timestamp":1675254575000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages Recommendation"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3773-4097","authenticated-orcid":false,"given":"Guixiang","family":"Zhu","sequence":"first","affiliation":[{"name":"Nanjing University of Finance and Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9942-3243","authenticated-orcid":false,"given":"Jie","family":"Cao","sequence":"additional","affiliation":[{"name":"Hefei University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5537-8989","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing Forestry University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4726-7493","authenticated-orcid":false,"given":"Youquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7582-8203","authenticated-orcid":false,"given":"Zhan","family":"Bu","sequence":"additional","affiliation":[{"name":"Nanjing Audit University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0794-7040","authenticated-orcid":false,"given":"Shuxin","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangxi University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-4199","authenticated-orcid":false,"given":"Jianqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Jiangxi University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5265-8355","authenticated-orcid":false,"given":"Zhiping","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu United Credit Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"11","volume-title":"Proceedings of the 2019 IEEE International Conference on Data Mining","author":"Altaf Basmah","year":"2019","unstructured":"Basmah Altaf, Uchenna Akujuobi, Lu Yu, and Xiangliang Zhang. 2019. 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