Collaborative filtering and inference rules for context-aware learning object recommendation

Interactive Technology and Smart Education

Volumn 2, Issue 3, 2005, Pages 179-188

  Lemire, Daniel ^a   Boley, Harold ^b   McGrath, Sean ^c   Ball, Marcel ^c  

^a UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

^b NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^c UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

Collaborative Filtering; Inference Rules; Learning Objects; Recommender Systems; RuleML; Semantic Web; Slope One

Indexed keywords

EID: 84993012356     PISSN: 17415659     EISSN: 17588510     Source Type: Journal    
DOI: 10.1108/17415650580000043     Document Type: Article

References (39)

1. Anderson, M., Ball, M., Boley, H., Greene, S., Howse, N., Lemire, D. and McGrath, S. (2003) RACOFI: A Rule-Applying Collaborative Filtering System. pp 53-72 of Proc. COLA'03. IEEE/WIC. Halifax, Canada.
2. Balabanovic, M. and Shoham, Y. (1997) Fab: Contentbased, collaborative recommendation. Communication of the ACM, 40(3), pp 66-72.
3. Ball, M., Boley, H., Hirtle, D., Mei, J. and Spencer, B. (2005) Implementing RuleML Using Schemas, Translators, and Bidirectional Interpreters. W3C Workshop on Rule Languages for Interoperability. Published at http://www.w3.org/2004/12/rulesws/ paper/49/. April 2005, Washington, D.C., USA
4. Berners-Lee, T. (1998) A roadmap to the Semantic Web. http://www.w3.org/DesignIssues/Semantic.html.
5. Bhavsar, Vi. C., Boley, H. and Yang, L. (2003) A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in e-Business Environments. pp53-72 of: Proc. Business Agents and the Semantic Web (BASeWEB) Workshop. Fredericton, Canada. NRC 45836.
6. Bhavsar, V. C., Boley, H., Hirtle, D., Singh, A., Sun, Z. and Yang, L. A Match-Making System for Learners and Learning Objects. Learning & Leading with Technology (To appear).
7. Boley, H. (2003) Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses, and Order-Sorted Terms. pp 32-40 of Rules and Rule Markup Languages for the Semantic Web (RuleML-2003). Sanibel Island, Florida, LNCS 2876, Springer-Verlag.
8. Breese, J. S., Heckerman, D. and Kadie, C. (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Fourteenth Conference on Uncertainty in AI. Los Angeles, USA, Morgan Kaufmann.
9. Codd, E. F., Codd, S. and Salley, C. (1993) Providing OLAP (On-line Analytical Processing) to User- Analysts: An IT Mandate. Tech. rept. E. F. Codd & Associates. USA.
10. Downes, S., Fournier, H. and Regoui, C. (2004). Projecting Quality. Presented at MADLaT. Winnipeg, Canada.
11. Downes, S. (2001) Learning Objects: Resources For Distance Education Worldwide. International Review of Research in Open and Distance Learning. 2 (1), 1-34.
12. Downes, S. (2002) Topic Representation and Learning Object Metadata. Published at http://www.downes.ca/cgibin/ website/view.cgi?dbs=Article&key=1011985735.
13. Downes, S. (2003) Meaning, Use and Metadata. Published at http://www.downes.ca/cgi-bin/website/view.cgi? dbs=Article&key=1061841698.
14. Fiaidhi, J. (2004) RecoSearch: A Model for Collaboratively Filtering Java Learning Objects. ITL, 1(7).
15. Fiaidhi, J., Passi, K. and Mohammed, S. (2004) Developing a Framework for Learning Objects Search Engine. Internet Computing'04, pp 1-7.
16. Gibbons, A. S., Nelson, J. and Richards, R. (2002) The Instructional Use of Learning Objects. Agency for Instructional Technology. Chap. The Nature and Origin of Instructional Objects.
17. Goldberg, D., Nichols, D., Oki, B. M. and Terry, D. (1992) Using Collaborative Filtering to Weave and Information Tapestry. CACM, 35(12), pp 61-70.
18. Han, K., Kumar, V. and Nesbit, J. C. (2003) Rating learning object quality with bayesian belief networks. E-Learn.
19. Herlocker, J., Konstan, J., Borchers, A. and Riedl, J. (1999) An Algorithmic Framework for Performing Collaborative Filtering. Proc. of Research and Development in Information Retrieval. Melbourne, Australia.
20. Hodgins W., Duval E. et al. (2002) Draft Standard for Learning Object Metadata, IEEE 1484.12.1-2002”, http://ltsc.ieee.org/wg12/files/LOM_1484_ 12_1_v1_Final_Draft.pdf.
21. Karypis, G. (2000) Evaluation of Item-Based Top-N Recommendation Algorithms. Tech. rept. 00-046. University of Minnesota, Department of Computer Science.
22. Kaser, O. and Lemire, D. (2003) Attribute Value Reordering for Efficient Hybrid OLAP. pp 1-8 of DOLAP'03. New Orleans, Louisiana, USA.
23. Koivunen, M.-R. and Miller, E. (2001) W3C Semantic Web Activity. Semantic Web Kick-off Seminar. Boston, USA.
24. Lemire, D. (2002) Wavelet-Based Relative Prefix Sum Methods for Range Sum Queries in Data Cubes. pp 1-8 of CASCON'02. Toronto, Canada.
25. Lemire, D. (2005) Scale and Translation Invariant Collaborative Filtering Systems. Information Retrieval, 8(1), 129-150.
26. Lemire, D. and Maclachlan, A. (2005) Slope One Predictors for Online Rating-Based Collaborative Filtering. In: SIAM Data Mining. NewPort Beach, California, USA.
27. Linden, G., Smith, B. and York, J. (2003) Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76-80.
28. Lundgren-Cayrol, K., Paquette, G., Miara, A., Rivard, J., Bergeron, F. and Rosca, I. (2001) Explor@ Advisory Agent: Tracing the Student's Trail. WebNet'01 Conference.
29. Melville, P., Mooney, R. J. and Nagarajan, R. (2002) Content-Boosted Collaborative Filtering for Improved Recommendations. pp 187-1992 of: AAAI/IAAI.
30. Mendelzon, A. O. (1998) The Web is not a Database. Workshop on Web Information and Data Management, pp 22-32.
31. Nesbit, J., Belfer, K. and Vargo, J. (2002) A Convergent Participation Model for Evaluation of Learning Objects. Canadian Journal of Learning and Technology, 28(3).
32. Ng, R., Wagner, A. and Yin, Y. (2001) Iceberg-cube Computation with PC Clusters. ACM SIGMOD 2001.
33. Pennock, D. M. and Horvitz, E. (1999) Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach. IJCAI-99.
34. Raîche, G. (2000) La distribution d'échantillonnage de l'estimateur du niveau d'habileté en testing adaptatif en fonction de deux règles d'arrêt : selon l'erreur-type et selon le nombre d'items administrés. PhD thesis, Université de Montréal.
35. Recker, M., Walker, A. and Lawless, K. (2003) What do you recommend? Implementation and analyses of collaborative filtering of Web resources for education. Instructional Science, 31(4), 229-316.
36. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. (1994) Grouplens: An open architecture for collaborative filtering of netnews. Proc. ACM Computer Supported Cooperative Work, pp 175-186.
37. Sarwar, B. M., Karypis, G., Konstan, J. A. and Riedl, J. (2001) Item-based Collaborative Filtering Recommender Algorithms. WWW10. Miami, Florida, USA.
38. Weatherley, J., Sumner, T., Khoo, M., Wright, M. and Hoffmann, M. (2002) Partnership reviewing: a cooperative approach for peer review of complex educational resources. Proceedings of the second ACM/IEEECS joint conference on Digital libraries. ACM Press, pp 106-114.
39. Weiss, S. M. and Indurkhya, N. (2001) Lightweight Collaborative Filtering Method for Binary Encoded Data. PKDD ‘01. Freiburg, Germany.