creators_name: Lemire, Daniel
creators_name: Boley, Harold
editors_name: Ghorbani, Ali
editors_name: Marsh, Stephen
type: confpaper
datestamp: 2003-09-19
lastmod: 2011-03-11 08:55:21
metadata_visibility: show
title: RACOFI: A Rule-Applying Collaborative Filtering System
ispublished: inpress
subjects: comp-sci-mach-learn
subjects: comp-sci-art-intel
full_text_status: public
abstract: In this paper we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at http://racofi.elg.ca. .
date: 2003
date_type: published
refereed: TRUE
citation:   Lemire, Daniel and Boley, Harold  (2003) RACOFI: A Rule-Applying Collaborative Filtering System.  [Conference Paper]    (In Press)  
document_url: http://cogprints.org/3166/1/racofi_nrc.pdf