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User Modeling and User-Adapted Interaction

, Volume 7, Issue 4, pp 279–304 | Cite as

Feature-based and Clique-based User Models for Movie Selection: A Comparative Study

  • Joshua Alspector
  • Aleksander Koicz
  • N. Karunanithi
Article

Abstract

The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an ‘information filtering agent’ which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available.

User modeling information filtering collaborative filtering feature extraction neural networks linear models regression trees bagging CART 
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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Joshua Alspector
    • 1
  • Aleksander Koicz
    • 1
  • N. Karunanithi
    • 2
  1. 1.ECE DepartmentUniversity of ColoradoColorado SpringsU.S.A.
  2. 2.IF-319BBellcoreMorristown

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