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

Pozo, M; Chiky,, R; Meziane, F; Metais, E

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.

Citation

Pozo, M., Chiky,, R., Meziane, F., & Metais, E. (2017, September). Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions. Presented at 9th International Conference on Computational Collective Intelligence, Nicosia, Cyprus

• Presentation Conference Type: Other
• Conference Name: 9th International Conference on Computational Collective Intelligence
• Conference Location: Nicosia, Cyprus
• Start Date: Sep 27, 2017
• End Date: Sep 29, 2017
• Acceptance Date: Aug 28, 2017
• Online Publication Date: Sep 7, 2017
• Publication Date: Sep 7, 2017
• Deposit Date: Sep 14, 2017
• Publicly Available Date: Sep 7, 2018
• Series Title: Lecture Notes in Computer Science
• Series Number: 10448
• Book Title: Computational Collective Intelligence 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part I
• ISBN: 9783319670737;-9783319670744
• DOI: https://doi.org/10.1007/978-3-319-67074-4_14
• Publisher URL: http://dx.doi.org/10.1007/978-3-319-67074-4_14
• Related Public URLs: http://cyprusconferences.org/iccci2017/index.html; https://link.springer.com/book/10.1007/978-3-319-67074-4
• Additional Information: Event Type : Conference