SIRUP: Serendipity In Recommendations via User Perceptions

V. Maccatrozzo, L.M. Aroyo, Manon Terstall, Guus Schreiber

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Intelligent User Interfaces
Place of PublicationNew York, NY, USA
PublisherACM
Pages35-44
DOIs
Publication statusPublished - Mar 2017

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Recommender systems

Keywords

  • Recommender System
  • Curiosity
  • Television/Video
  • Entertainment
  • Design Methods
  • Personalization
  • Qualitative Methods
  • User and Cognitive models
  • User Studies
  • Serendipity

Cite this

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 (pp. 35-44). New York, NY, USA: ACM. https://doi.org/10.1145/3025171.3025185
Maccatrozzo, V. ; Aroyo, L.M. ; Terstall, Manon ; Schreiber, Guus. / SIRUP: Serendipity In Recommendations via User Perceptions. Proceedings of the 22nd International Conference on Intelligent User Interfaces. New York, NY, USA : ACM, 2017. pp. 35-44
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abstract = "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.",
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Maccatrozzo, V, Aroyo, LM, 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

SIRUP: Serendipity In Recommendations via User Perceptions. / Maccatrozzo, V.; Aroyo, L.M.; Terstall, Manon; Schreiber, Guus.

Proceedings of the 22nd International Conference on Intelligent User Interfaces. New York, NY, USA : ACM, 2017. p. 35-44.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Maccatrozzo V, Aroyo LM, Terstall M, Schreiber G. SIRUP: Serendipity In Recommendations via User Perceptions. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. New York, NY, USA: ACM. 2017. p. 35-44 https://doi.org/10.1145/3025171.3025185