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

Working paper

Meziane, F. 2018. Exploiting Past Users’ Interests and Predictions in an Active Learning Method for Dealing with Cold Start in Recommender Systems. MDPI AG. https://doi.org/10.20944/preprints201803.0253.v1
AuthorsMeziane, F.
TypeWorking paper
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.

Keywordscold start; recommender systems; active learning; collaborative filtering
Year2018
PublisherMDPI AG
ISSN22279709
Digital Object Identifier (DOI)https://doi.org/10.20944/preprints201803.0253.v1
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85061242060&partnerID=MN8TOARS
Output statusPublished
Publication dates
Online15 Aug 2018
Publication process dates
Accepted06 Aug 2018
Deposited05 Jun 2023
JournalInformatics
Journal citationVol 5 (Issue 3), p. 35
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https://repository.derby.ac.uk/item/9z140/exploiting-past-users-rsquo-interests-and-predictions-in-an-nbsp-active-learning-method-for-dealing-with-cold-start-nbsp-in-recommender-systems

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