Lemire and Boley (2003) RACOFI: A Rule-Applying Collaborative Filtering System

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RACOFI: A Rule-Applying Collaborative Filtering System

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CONTRIBUTORS:

	Author:	Lemire, Daniel (Université du Québec à Montréal (UQAM))
	Author:	Boley, Harold

PROCEEDINGS TITLE:

IEEE/WIC COLA'03

YEAR:

2003

PUB TYPE:

Conference Paper in Proceedings

PAGES:

n/a - n/a

SUBJECT(S):

Recommender system, ruleml, collaborative filtering, scale and translation, regression.

DISCIPLINE:

No discipline assigned

HTTP:

http://www.ondelette.com/lemire/abstracts/COLA2003.html

LANGUAGE:

None

PUB ID:

103-399-843 (Last edited on 2004/02/18 19:49:05 US/Mountain)

SPONSOR(S):

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.

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