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Active Learning in Recommender Systems

Chapter

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.

Keywords

Active Learn Recommender System Decision Boundary Training Point Normalize Discount Cumulative Gain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University of Electro-CommunicationsTokyoJapan
  2. 2.Free University of Bozen-BolzanoBolzanoItaly
  3. 3.Tokyo Institute of TechnologyTokyoJapan

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