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Greedy Recommending Is Not Always Optimal

  • Maarten van Someren
  • Vera Hollink
  • Stephan ten Hagen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3209)

Abstract

Recommender systems suggest objects to users. One form recommends documents or other objects to users searching information on a web site. A recommender system can use data about a user to recommend information, for example web pages. Current methods for recommending are aimed at optimising single recommendations. However, usually a series of interactions is needed to find the desired information.

Here we argue that in interactive recommending a series of normal, ‘greedy’, recommendings is not the strategy that minimises the number of steps in the search. Greedy sequential recommending conflicts with the need to explore the entire space of user preferences and may lead to recommending series that require more steps (mouse clicks) from the user than necessary. We illustrate this with an example, analyse when this is so and outline when greedy recommending is not the most efficient.

Keywords

Recommender System Target Object User Preference User Satisfaction Content Object 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Maarten van Someren
    • 1
  • Vera Hollink
    • 1
  • Stephan ten Hagen
    • 2
  1. 1.Dept. of Social Science InformaticsUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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