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International Conference on Electronic Commerce and Web Technologies

EC-Web 2014: E-Commerce and Web Technologies pp 113–124Cite as

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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Author information

Authors and Affiliations

  1. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Mehdi Elahi & Francesco Ricci

  2. University of Electro-Communications, Tokyo, Japan

    Neil Rubens

Authors
  1. Mehdi Elahi
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  2. Francesco Ricci
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  3. Neil Rubens
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Editor information

Editors and Affiliations

  1. E-Business and Web Science Research Group, Universität der Bundeswehr, Neubiberg, Germany

    Martin Hepp

  2. Department of Software Engineering, Shenkar College of Engineering and Design, Ramat Gan, Israel

    Yigal Hoffner

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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

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