Feature-based and Clique-based User Models for Movie Selection: A Comparative Study |
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Authors: | Alspector Joshua Koicz Aleksander Karunanithi N |
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Affiliation: | (1) ECE Department, University of Colorado, Colorado Springs, Colorado, 90918, U.S.A.;(2) IF-319B, Bellcore, 445 South Street, Morristown, NJ, 07960 |
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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.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | User modeling information filtering collaborative filtering feature extraction neural networks linear models regression trees bagging CART |
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