A fully automated recommender system using collaborative filters

Authors:
Wadee S. Al halabi;Miroslav Kubat;Moiez Tapia
Affiliations:
Computer Engineering University of Miami, Miami, FL;Computer Engineering University of Miami, Coral Gables, FL;Computer Engineering University of Miami, Coral Gables, FL
Venue:
CIIT '07 The Sixth IASTED International Conference on Communications, Internet, and Information Technology
Year:
2007

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Abstract

Recommender systems represent valuable marketing tools in e-commerce. No wonder that this field has enjoyed quite some attention from the scientific community that seeks to develop algorithms that optimize the performance of recommender systems based on the analysis of historical data. In the research reported here, we experimented with a mechanism that combines traditional collaborative filtering approaches with explicit rating. This paper describes the technique and illustrates its behavior in a test-bed we created from real-world data. The results are promising.