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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/67B9A36C-D414-4469-B473-CFA10E45A9DB | - |
| Αναγνωριστικό | https://doi.org/10.1145/3549737.3549772 | - |
| Αναγνωριστικό | https://dl.acm.org/doi/10.1145/3549737.3549772 | - |
| Γλώσσα | en | - |
| Μέγεθος | 10 pages | en |
| Τίτλος | Content-based recommendations using similarity distance measures with application in the tourism domain | en |
| Δημιουργός | Ziogas Ioannis-Panagiotis | en |
| Δημιουργός | Ζιωγας Ιωαννης-Παναγιωτης | el |
| Δημιουργός | Streviniotis Errikos | en |
| Δημιουργός | Στρεβινιωτης Ερρικος | el |
| Δημιουργός | Papadakis Harris | en |
| Δημιουργός | Chalkiadakis Georgios | en |
| Δημιουργός | Χαλκιαδακης Γεωργιος | el |
| Εκδότης | Association for Computing Machinery (ACM) | en |
| Περιγραφή | This 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 |
| Περίληψη | In 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2024-09-19 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Recommender systems | en |
| Θεματική Κατηγορία | Content-based | en |
| Θεματική Κατηγορία | Hierarchies | en |
| Θεματική Κατηγορία | Distance measures | en |
| Βιβλιογραφική Αναφορά | I.-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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