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.
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Acknowledgments
This work has been supported by FIORA project, and funded by “DGCIS” and “Conseil Regional de l’Île de France”.
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Pozo, M., Chiky, R., Meziane, F., Métais, E. (2017). Enhancing New User Cold-Start Based on Decision Trees Active Learning by Using Past Warm-Users Predictions. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_14
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