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Open AccessArticle

Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems

1
ISEP-LISITE, 75014 Paris, France
2
University of Salford, Greater Manchester M5 4WT, UK
3
CNAM-CEDRIC, 75003 Paris, France
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in ICCCI 2017.
Informatics 2018, 5(3), 35; https://doi.org/10.3390/informatics5030035
Received: 29 March 2018 / Revised: 2 August 2018 / Accepted: 6 August 2018 / Published: 15 August 2018
(This article belongs to the Special Issue Advances in Recommender Systems)
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues. View Full-Text
Keywords: cold start; recommender systems; active learning; collaborative filtering cold start; recommender systems; active learning; collaborative filtering
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Pozo, M.; Chiky, R.; Meziane, F.; Métais, E. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. Informatics 2018, 5, 35.

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