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: 9th International Conference on Computational Collective Intelligence, 27 - 29 September 2017, Nicosia, Cyprus.

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Abstract

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: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Computational Collective Intelligence 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part I
Publisher: Springer
Series Name: Lecture Notes in Computer Science
ISBN: 9783319670737; 9783319670744
ISSN: 0302-9743
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 14 Sep 2017 11:37
Last Modified: 16 Dec 2019 11:15
URI: http://usir.salford.ac.uk/id/eprint/43750

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