Recommender Systems for Multimedia Libraries: An Evaluation of Different Models for Datamining Usage Data
Date of Award
Master of Science (MS)
Call Number for Print
Special Collections LD4331.C65 A73 2004
Many recommender systems exist today to help users deal with the large growth in the amount of information available in the Internet. Most of these recommender systems use collaborative filtering or content-based techniques to present new material that would be of interest to a user. While these methods have proven to be effective, they have not been designed specifically for multimedia collections. In this study we present a new method to find recommendations that is not dependent on traditional Information Retrieval (IR) methods and compare it to algorithms that do rely on traditional IR methods. We evaluated these algorithms using an open source video test collection. Our study shows that our proposed approach outperforms a collaborative filtering algorithm and performs just as well as an algorithm that uses the Vector Space Model to find recommendations.
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Araujo, Raquel O..
"Recommender Systems for Multimedia Libraries: An Evaluation of Different Models for Datamining Usage Data"
(2004). Master of Science (MS), Thesis, Computer Science, Old Dominion University, DOI: 10.25777/2866-4j44