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: 33ème conférence sur la Gestion de Données — Principes, Technologies et Applications, 14-17 November 2017, Nancy, France.

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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 well performed. This issue is commonly encountered in techniques based on collaborative filtering, as they mainly rely on the feedback of users and items. This paper focuses on the active learning techniques based on collaborative filtering and using decision trees to address the new user cold-start in recommender systems. These techniques propose to interact with new users by asking them to rate sequentially a few items while the system tries to detect their preferences. Their main goal is to find out the best recognizable items for the new user in order to get very informative user’s feedback. 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. This is specially interesting in datasets with a low number of users.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of ACM conference, Nancy, France, November 2017 (BDA’17)
Publisher: ACM
Related URLs:
Depositing User: Prof Farid Meziane
Date Deposited: 14 Sep 2017 11:44
Last Modified: 15 Feb 2022 22:26
URI: https://usir.salford.ac.uk/id/eprint/43751

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