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Active Learning in Recommender Systems

  • Chapter
Recommender Systems Handbook

Abstract

In Recommender Systems (RS), a user’s preferences are expressed in terms of rated items, where incorporating each rating may improve the RS’s predictive accuracy. In addition to a user rating items at-will (a passive process), RSs may also actively elicit the user to rate items, a process known as Active Learning (AL). However, the number of interactions between the RS and the user is still limited. One aim of AL is therefore the selection of items whose ratings are likely to provide the most information about the user’s preferences. In this chapter, we provide an overview of AL within RSs, discuss general objectives and considerations, and then summarize a variety of methods commonly employed. AL methods are categorized based on our interpretation of their primary motivation/goal, and then sub-classified into two commonly classified types, instance-based and model-based, for easier comprehension. We conclude the chapter by outlining ways in which AL methods could be evaluated, and provide a brief summary of methods performance.

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Notes

  1. 1.

    Unless our goal is to learn a kind of micro-preference, which we can define as a person’s tendency to be more “picky” concerning alternatives close to one another in an genre they like.

  2. 2.

    Supplementary materials on Active Learning can be found at: http://ActiveIntelligence.org.

  3. 3.

    This may be dependent on the specific prediction method used in the RS.

  4. 4.

    For comparing of recommendations with various lengths, normalized Discounted Cumulative Gain (NDCG) is frequently used.

  5. 5.

    The way in which an item is represented depends on the RS and the underlying predictive method. In Collaborative Filtering based approaches items could represented through the ratings of the users, or, in content based RSs, items could be represented through their descriptions.

  6. 6.

    If the ordinal properties of the labels are considered, it is referred to as Ordinal Classification.

  7. 7.

    Defining a neighbor as a similar item is also feasible depending on the method.

  8. 8.

    Recently it has also been proposed to utilize transfer learning for leveraging pre-existing labeled data from related tasks to improve the performance of an active learning algorithm [34, 69].

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Authors and Affiliations

  1. University of Electro-Communications, Tokyo, Japan

    Neil Rubens

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

    Mehdi Elahi

  3. Tokyo Institute of Technology, Tokyo, Japan

    Masashi Sugiyama & Dain Kaplan

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  1. Neil Rubens
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  2. Mehdi Elahi
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  3. Masashi Sugiyama
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  4. Dain Kaplan
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Correspondence to Neil Rubens .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Rubens, N., Elahi, M., Sugiyama, M., Kaplan, D. (2015). Active Learning in Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_24

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