Getting to know you: Learning new user preferences in recommender systems

Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K. Lam, Sean M. McNee, Joseph A Konstan, John Riedl

Research output: Contribution to conferencePaperpeer-review

359 Scopus citations

Abstract

Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large preexisting user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Original languageEnglish (US)
Pages127-134
Number of pages8
StatePublished - Jan 1 2002
Event2002 International Conference on intelligent User Interfaces (IUI 02) - San Francisca, CA, United States
Duration: Jan 13 2002Jan 16 2002

Other

Other2002 International Conference on intelligent User Interfaces (IUI 02)
CountryUnited States
CitySan Francisca, CA
Period1/13/021/16/02

Keywords

  • Collaborative filtering
  • Entropy
  • Information filtering
  • Recommender systems
  • Startup problem
  • User modeling

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