Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions
Conference paper
Authors | Pozo, M, Chiky, R, Meziane, F. and Metais, E |
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Type | Conference paper |
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 well performed. This issue is commonly encountered in techniques based on collaborative filtering, as they mainly rely on the feedback |
Keywords | Active learning for recommender systems; ; Cold-start problem; ; Decision trees; ; New users problem |
Year | 2017 |
Conference | 9th International Conference, ICCCI 2017 |
Journal | Lecture Notes in Computer Science |
Publisher | Springer |
ISSN | 03029743 |
1611-3349 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-67074-4_14 |
Web address (URL) | https://project.inria.fr/bda2017/ |
Journal citation | Vol 10448, pp. 137 - 147 |
ISBN | 978-3-319-67074-4 |
Web address (URL) of conference proceedings | https://link.springer.com/book/10.1007/978-3-319-67074-4 |
Output status | Published |
Publication dates | |
Online | 07 Sep 2017 |
Online | 18 Sep 2017 |
08 Sep 2017 | |
Publication process dates | |
Deposited | 05 Jun 2023 |
https://repository.derby.ac.uk/item/9z162/enhancing-new-user-cold-start-based-on-decision-trees-active-learning-by-using-past-warm-users-predictions
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