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Active Learning in Collaborative Filtering Recommender Systems

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Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 188)

Abstract

In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active learning Strategies are used to selectively choose the items to present to the user in order to acquire her ratings and ultimately improve the recommendation accuracy. In this survey article, we review recent active learning techniques for collaborative filtering along two dimensions: (a) whether the system requested ratings are personalised or not, and, (b) whether active learning is guided by one criterion (heuristic) or multiple criteria.

Keywords

  • recommender systems
  • cold start
  • active learning

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  • DOI: 10.1007/978-3-319-10491-1_12
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Elahi, M., Ricci, F., Rubens, N. (2014). Active Learning in Collaborative Filtering Recommender Systems. In: Hepp, M., Hoffner, Y. (eds) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-10491-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-10491-1_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10490-4

  • Online ISBN: 978-3-319-10491-1

  • eBook Packages: Computer ScienceComputer Science (R0)