Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions

Pozo, M, Chiky,, R, Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914 and Metais, E 2017, 'Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions' , in: ICCCI 2017 : Computational Collective Intelligence , Lecture Notes in Computer Science, 1 , Springer, pp. 137-147.

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The cold-start is the situation in which the recommender system has no or not enough information about the (new) users/items, i.e. their ratings/feedback; hence, the recommendations are not accurate. Active learning techniques for recommender systems propose to interact with new users by asking them to rate sequentially a few items while the system tries to detect her preferences. This bootstraps recommender systems and alleviate the new user cold-start. Compared to current state of the art, the presented approach takes into account the users' ratings predictions in addition to the available users' ratings. The experimentation shows that our approach achieves better performance in terms of precision and limits the number of questions asked to the users.

Item Type: Book Section
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Proceedings of ICCCI 2017
Publisher: Springer
Series Name: Lecture Notes in Computer Science
ISBN: 9783319670737
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 14 Sep 2017 11:37
Last Modified: 13 Mar 2019 08:02
URI: http://usir.salford.ac.uk/id/eprint/43750

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