Multi-criteria tensor model for tourism recommender systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, M. | - |
dc.contributor.author | Jung, J.J. | - |
dc.date.accessioned | 2021-05-20T08:40:27Z | - |
dc.date.available | 2021-05-20T08:40:27Z | - |
dc.date.issued | 2021-05-15 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.issn | 1873-6793 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44029 | - |
dc.description.abstract | Many tourism recommender systems have been studied to offer users the items meeting their interests. However, it is a non-trivial task to reflect the multi-criteria ratings and the cultural differences, which significantly influence users’ reviews of tourism facilities, into recommendation services. This paper proposes two “single tensor” models, consisting of users (or countries), items, multi-criteria ratings, and cultural groups, in order to consider simultaneously an inherent structure and interrelations of these factors into recommendation processes. With one Tripadvisor dataset, including 13 K users from 120 countries, experiments demonstrated that, in terms of MAE, the two proposed models for user and country give an improvement of 21.31% and 7.11% than other collaborative filtering and multi-criteria recommendation techniques. Besides, there were the positive influences of multiple-criteria ratings and cultural group factors on recommendation performances. The comparative analysis of several variants of the proposed models showed that considering Western and Eastern cultures is appropriate for improving predictive performances and their stability. © 2020 Elsevier Ltd | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Multi-criteria tensor model for tourism recommender systems | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.eswa.2020.114537 | - |
dc.identifier.bibliographicCitation | Expert Systems with Applications, v.170 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000633043100007 | - |
dc.identifier.scopusid | 2-s2.0-85099448346 | - |
dc.citation.title | Expert Systems with Applications | - |
dc.citation.volume | 170 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Cultural difference | - |
dc.subject.keywordAuthor | Multi-criteria recommendation | - |
dc.subject.keywordAuthor | Multi-dimensional model | - |
dc.subject.keywordAuthor | Tensor factorization | - |
dc.subject.keywordAuthor | Tourism recommender system | - |
dc.subject.keywordAuthor | User preference modeling | - |
dc.subject.keywordPlus | Collaborative filtering | - |
dc.subject.keywordPlus | Recommender systems | - |
dc.subject.keywordPlus | Tensors | - |
dc.subject.keywordPlus | Comparative analysis | - |
dc.subject.keywordPlus | Cultural difference | - |
dc.subject.keywordPlus | Multi-criteria ratings | - |
dc.subject.keywordPlus | Multiple criteria | - |
dc.subject.keywordPlus | Non-trivial tasks | - |
dc.subject.keywordPlus | Predictive performance | - |
dc.subject.keywordPlus | Recommendation performance | - |
dc.subject.keywordPlus | Recommendation techniques | - |
dc.subject.keywordPlus | Tourism | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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