SIRUP: Serendipity In Recommendations via User Perceptions

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

doi:https://doi.org/10.1145/3025171.3025185

SIRUP: Serendipity In Recommendations via User Perceptions

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

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proceedings of the 22nd International Conference on Intelligent User Interfaces
Place of Publication	New York, NY, USA
Publisher	ACM
Pages	35-44
Number of pages	10
ISBN (Electronic)	9781450343480
DOIs	https://doi.org/10.1145/3025171.3025185
Publication status	Published - 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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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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