RACOFI: A Rule-Applying Collaborative Filtering System


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.


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


Michelle Anderson, Marcel Ball, Harold Boley, Stephen Greene, Nancy Howse, Daniel Lemire, Sean McGrath, RACOFI: A Rule-Applying Collaborative Filtering System, In Proc. IEEE/WIC COLA'03, Halifax, Canada, October 2003. (NRC 46507)

See COLA's home page.


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The paper is also available from Cogprints.


Download the java implementation of the collaborative filtering algorithms. It requires the gnu.trove package. New: COFI has been open sourced under GPL.

The paper relied on both OO RuleML and the COFI library. OO JREW will soon be open sourced, please check with Bruce.SpencerATnrc-crncDOTgcDOTca.

Sorry for the inconvenience, but the system is rather large and involves several components that cannot be easily packaged without substantial work. Thus, the entire system as it stands will probably never be made available on this page.


   author    = {Michelle Anderson and Marcel Ball and Harold Boley and Stephen Greene and Nancy Howse and Daniel Lemire and Sean McGrath},
   title     = {RACOFI: A Rule-Applying Collaborative Filtering System},
   booktitle = {Proceedings of COLA'03},
   organization = {IEEE/WIC},
   month     = {October},
   year      = {2003},
   url = {http://www.daniel-lemire.com/fr/documents/publications/racofi_nrc.pdf},


Many people contributed to this paper, but the two primary researchers are Harold Boley and Daniel Lemire who lead, respectively, the rule-based work and the collaborative filtering per se.

Related work


This paper is cited by the following papers:

  1. Matthew Garden, Gregory Dudek. 2006. Mixed Collaborative and Content-Based Filtering with User-Contributed Semantic Features. In Proceedings of the Twenty-First National Conference on Artificial Intelligence,2-8. Menlo Park, Calif.: AAAI Press.
  2. K. Kaji, K. Hirata, K. Nagao, A music recommendation system based on annotations about listeners' preferences and situations, Automated Production of Cross Media Content for Multi-Channel Distribution, 2005.
  3. Jon Dron, A Loophole in Moores Law of Transactional Distance, IEEE ICALT'04, pp. 41-45.
  4. J. Fiaidhi, K. Passi, and S. Mohammed, Developing a Framework for Learning Objects Search Engine, The 2004 International Conference on Internet Computing (IC04), Las Vegas, Nevada, USA, June 21-24, 2004.
  5. J. Fiaidhi, RecoSearch: A Model for Collaboratively Filtering Java Learning Objects, ITDL Vol 1. No. 7., 2004. (html, pdf)
  6. Stephen Marsh, infoDNA (Version 2) Agent Enhanced Trustworthy Distributed Information, PST'04, October 2004.
  7. Miguel-Ángel Sicilia, Elena García, On the Use of Bipolar Scales in Preference-Based Recommender Systems, E-Commerce and Web Technologies: 5th International Conference, EC-Web 2004, Zaragoza, Spain, August 2004.
  8. Matthew Garden, On the use of semantic feedback in recommender systems, M.Sc. Thesis in Computer Science, McGill University, August 2004.
  9. Harold Boley, Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses, and Order-Sorted Terms, RuleML2003, 2003.
  10. Katsuhiko Ka ji, Keiji Hirata, Katashi Nagao, An Online Music Recommendation System Based on Annotations about Listeners' Preference and Situation, IPSJ Sigmus'04, December 2004.
  11. A. Maclachlan and H. Boley, Semantic Web Rules for Business Information, Web Technologies, Applications, and Services, 2005.
  12. Dron, J., Epimethean information systems: harnessing the power of the collective in e-learning, International Journal of Information Technology and Management, Vol. 4, Number 4, pages 392-404, 2005.

This paper cites the following paper:

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