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 ORCID: https://orcid.org/0000-0001-9811-6914 and 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) , p. 35.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Informatics
Publisher: MDPI
ISSN: 2227-9709
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 15 Aug 2018 12:56
Last Modified: 07 Jun 2019 12:00
URI: http://usir.salford.ac.uk/id/eprint/48054

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