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Content-based recommendations using similarity distance measures with application in the tourism domain

Ziogas Ioannis-Panagiotis, Streviniotis Errikos, Papadakis Harris, Chalkiadakis Georgios

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URIhttp://purl.tuc.gr/dl/dias/67B9A36C-D414-4469-B473-CFA10E45A9DB-
Identifierhttps://doi.org/10.1145/3549737.3549772-
Identifierhttps://dl.acm.org/doi/10.1145/3549737.3549772-
Languageen-
Extent10 pagesen
TitleContent-based recommendations using similarity distance measures with application in the tourism domainen
CreatorZiogas Ioannis-Panagiotisen
CreatorΖιωγας Ιωαννης-Παναγιωτηςel
CreatorStreviniotis Errikosen
CreatorΣτρεβινιωτης Ερρικοςel
CreatorPapadakis Harrisen
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
PublisherAssociation for Computing Machinery (ACM)en
DescriptionThis research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH -CREATE-INNOVATE B cycle (project code: T2EDK-03135). E. Streviniotis was also supported by the Onassis Foundation - Scholarship ID: G ZR 012-1/2021-2022.en
Content SummaryIn this paper, we explore the use of similarity distance measures for Content-based recommendations for touristic attractions. First, we study ways of deploying hierarchies of points of interests (POIs) and operate upon them with well-known similarity distance measures originating in the text analysis domain. Then, we progressively build three novel, hierarchy-free, similarity measures, and discuss their strengths and weaknesses. We end up with a measure, the Weighted Extended Jaccard Similarity (WEJS) that combines information regarding the user interests (in the form of user preference-related weights applied on the items’ features) and specific items’ characteristics (in the form of particular values for the items’ features). As such, the use of WEJS allows the provision of recommendations that are effectively personalized. Interestingly, though it is a hierarchy-free measure, it is able to recommend items based on others that would naturally appear close in a features-based POIs hierarchy; while at the same time it is able to capture similarities among items that would be distant to each other in any hierarchy built solely based on the POIs’ features. Our systematic experimental evaluation on a real-world dataset showcases the benefits and limitations of the various measures, and confirms the effectiveness of WEJS in offering “rich” and personalized recommendations.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-09-19-
Date of Publication2022-
SubjectRecommender systemsen
SubjectContent-baseden
SubjectHierarchiesen
SubjectDistance measuresen
Bibliographic CitationI.-P. Ziogas, E. Streviniotis, H. Papadakis and G. Chalkiadakis, “Content-based recommendations using similarity distance measures with application in the tourism domain,” in Proceedings of the 12th Hellenic Conference on Artificial Intelligence (SETN 2022), Corfu, Greece, 2022. doi: 10.1145/3549737.3549772.en

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