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Authors: Miriam Badran 1 ; Jacques Abdo 2 ; Wissam Jurdi 2 and Jacques Demerjian 3

Affiliations: 1 Department of Computer Science, Notre Dame University, Zook Mosbeh and Lebanon ; 2 Department of Computer Science, Notre Dame University, Deir el Qamar and Lebanon ; 3 LARIFA-EDST Laboratory, Faculty of Science, Lebanese University, Fanar and Lebanon

ISBN: 978-989-758-350-6

ISSN: 2184-433X

Keyword(s): Serendipity, Accuracy, Recommender System.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Human-Computer Interaction ; Intelligent User Interfaces ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Recommender systems are nowadays widely implemented in order to predict the potential objects of interest for the user. With the wide world of the internet, these systems are necessary to limit the problem of information overload and make the user’s internet surfing a more agreeable experience. However, a very accurate recommender system creates a problem of over-personalization where there is no place for adventure and unexpected discoveries: the user will be trapped in filter bubbles and echo rooms. Serendipity is a beneficial discovery that happens by accident. Used alone, serendipity can be easily confused with randomness; this takes us back to the original problem of information overload. Hypothetically, combining accurate and serendipitous recommendations will result in a higher user satisfaction. The aim of this paper is to prove the following concept: including some serendipity at the cost of profile accuracy will result in a higher user satisfaction and is, therefore, more fa vourable to implement. We will be testing a first measure implementation of serendipity on an offline dataset that lacks serendipity implementation. By varying the ratio of accuracy and serendipity in the recommendation list, we will reach the optimal number of serendipitous recommendations to be included in an accurate list. (More)

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Paper citation in several formats:
Badran, M.; Abdo, J.; Jurdi, W. and Demerjian, J. (2019). Adaptive Serendipity for Recommender Systems: Let It Find You. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6 ISSN 2184-433X, pages 739-745. DOI: 10.5220/0007409507390745

@conference{icaart19,
author={Miriam Badran. and Jacques Abdo. and Wissam Jurdi. and Jacques Demerjian.},
title={Adaptive Serendipity for Recommender Systems: Let It Find You},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={739-745},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007409507390745},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Adaptive Serendipity for Recommender Systems: Let It Find You
SN - 978-989-758-350-6
IS - 2184-433X
AU - Badran, M.
AU - Abdo, J.
AU - Jurdi, W.
AU - Demerjian, J.
PY - 2019
SP - 739
EP - 745
DO - 10.5220/0007409507390745

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