@inproceedings{07dc756f06144557af60aedfff46cff0,
title = "A fully automated recommender system using collaborative filters",
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.",
keywords = "Collaborative filtering, Internet tools, Recommender systems, User profile",
author = "{Al Halabi}, {Wadee S.} and Miroslav Kubat and Moiez Tapia",
year = "2007",
language = "English (US)",
isbn = "9780889866744",
series = "Proceedings of the Sixth IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007",
pages = "226--231",
booktitle = "Proceedings of the Sixth IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007",
note = "6th IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007 ; Conference date: 02-07-2007 Through 04-07-2007",
}