Exploiting past users’ interests and predictions in an active learning method for dealing with cold start in recommender systems

Pozo, M; Chiky, R; Meziane, F; Métais, E

Abstract

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.

Citation

Pozo, M., Chiky, R., Meziane, F., & Métais, E. (2018). Exploiting past users’ interests and predictions in an active learning method for dealing with cold start in recommender systems. Informatics, 5(3), 35. https://doi.org/10.3390/informatics5030035

• Journal Article Type: Article
• Acceptance Date: Aug 6, 2018
• Online Publication Date: Aug 15, 2018
• Publication Date: Aug 15, 2018
• Deposit Date: Aug 15, 2018
• Publicly Available Date: Aug 15, 2018
• Journal: Informatics
• Publisher: MDPI
• Volume: 5
• Issue: 3
• Pages: 35
• DOI: https://doi.org/10.3390/informatics5030035
• Publisher URL: http://www.mdpi.com/2227-9709/5/3/35
• Related Public URLs: http://www.mdpi.com/journal/informatics