SIRUP: Serendipity In Recommendations via User Perceptions
| Autor: | Lora Aroyo, Manon Terstall, Guus Schreiber, Valentina Maccatrozzo |
|---|---|
| Přispěvatelé: | Business Web and Media, Faculty of Sciences, Network Institute, Intelligent Information Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
User Studies
User and Cognitive models Qualitative Methods Computer science media_common.quotation_subject 02 engineering and technology Recommender system Personalization Human–computer interaction Entertainment 0202 electrical engineering electronic engineering information engineering Design Methods Recommender System Design methods media_common User profile Serendipity Novelty 020207 software engineering Linked data Television/Video Curiosity 020201 artificial intelligence & image processing |
| Zdroj: | Maccatrozzo, V, Aroyo, L M, Terstall, M & Schreiber, G 2017, SIRUP: Serendipity In Recommendations via User Perceptions . in Proceedings of the 22nd International Conference on Intelligent User Interfaces . ACM, New York, NY, USA, pp. 35-44 . https://doi.org/10.1145/3025171.3025185 Proceedings of the 22nd International Conference on Intelligent User Interfaces, 35-44 STARTPAGE=35;ENDPAGE=44;TITLE=Proceedings of the 22nd International Conference on Intelligent User Interfaces IUI |
| DOI: | 10.1145/3025171.3025185 |
| Popis: | In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision. |
| Databáze: | OpenAIRE |
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