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
Number of pages10
ISBN (Electronic)9781450343480
DOIs
Publication statusPublished - Mar 2017

Keywords

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

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