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2017 | OriginalPaper | Buchkapitel

Enhancing New User Cold-Start Based on Decision Trees Active Learning by Using Past Warm-Users Predictions

verfasst von : Manuel Pozo, Raja Chiky, Farid Meziane, Elisabeth Métais

Erschienen in: Computational Collective Intelligence

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Testing the Acceptability of Social Support Agents in Online Communities

Nächstes Kapitel An Efficient Parallel Method for Performing Concurrent Operations on Social Networks

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Titel: Enhancing New User Cold-Start Based on Decision Trees Active Learning by Using Past Warm-Users Predictions
verfasst von: Manuel Pozo
Raja Chiky
Farid Meziane
Elisabeth Métais
Verlag: Springer International Publishing
Buch: Computational Collective Intelligence
Print ISBN: 978-3-319-67073-7

Electronic ISBN: 978-3-319-67074-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67074-4_14

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