Authors:
Miriam El Khoury Badran
1
;
Jacques Bou Abdo
2
;
Wissam Jurdi
2
and
Jacques Bou 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
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 f
avourable 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.
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