Abstract
Nowadays, the World Wide Web offers public search services by a number of Internet search engine companies e.g. Google [16], Yahoo! [17], etc. They own their internal ranking algorithms, which may be designed for either general-purpose information and/or specific domains. In order to fight for bigger market share, they have developed advanced tools to facilitate the algorithms through the use of Relevance Feedback (RF) e.g. Google’s Toolbar. Experienced by the black-box tests of the RF toolbar, all in all, they can acquire simple and individual RF contribution. As to this point, in this paper, we have proposed a collaboratively shared Information Retrieval (IR) model to complement the conventional IR approach (i.e. objective) with the collaborative user contribution (i.e. subjective). Not only with RF and group relevance judgments, our proposed architecture and mechanisms provide a unified way to handle general purpose textual information (herein, we consider e-Learning related documents) and provide advanced access control features [15] to the overall system.
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References
Buckley, C., Salton, G., Allan, J.: The Effect of Adding Relevance Information in a Relevance Feedback Environment. In: Proc. 17th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval, Dublin, Ireland, pp. 292–300 (1994)
Harman, D.: Relevance Feedback Revisited. In: Proc. 5th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval. Copenhagen, Denmark, pp. 1–10 (1992)
Salton, G., Buckley, C.: Improving Retrieval Performance by Relevance Feedback. J. American Society for Information Science 41(4), 288–297 (1990)
Belew, R.: Rave Reviews: Acquiring Relevance Assessments from Multiple Users. In: Working Notes of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (1996)
Koenemann, J., Belkin, N.J.: A Case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness. In: Proc. ACM Conf. on Human Factors in Computing Systems, Zurich, Switzerland, vol. 1, pp. 205–212 (1996)
Aalbersberg, I.J.: Incremental Relevance Feedback. In: Proc. 15th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval, Copenhagen, Denmark, pp. 11–22 (1992)
Turtle, H., Croft, W.B.: Inference Networks for Document Retrieval. In: Proc. 13th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval, Brussels, Belgium, pp. 1–24 (1990)
Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: A Tour Guide for the World Wide Web. In: Proc. 15th Intl. Joint Conf. on Artificial Intelligence, Nagoya, Japan (1997)
Pazzani, M., Billsus, D., Muramatsu, J.: Syskill & Webert: Identifying Interesting Web Sites. In: Proc. 13th Annual National Conf. on Artificial Intelligence, Portland, OR, USA, pp. 54–61 (1996)
Lieberman, H.: Letizia: An Agent that Assists Web Browsing. In: Proc. 14th Intl. Joint Conf. on Artificial Intelligence, pp. 924–929 (1995)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Resnick, P., Varian, H.: Introduction: Special Issue on Collaborative Filtering. Communications of the ACM 40(3), 56–58 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-based Information in Recommendation. In: Proc. AAAI, Madison, WI, pp. 714–720 (1998)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating Word of Mouth. In: Proc. ACM Conf. on Human Factors in Computing Systems, Denver, CO, USA, pp. 210–217 (1995)
Chan, S.S.M., Li, Q., Pino, J.A.: VideoAcM: A Transitive and Temporal Access Control Mechanism for Collaborative Video Database Production Applications. Multimedia Tools and Applications: An Intl. J. Kluwer Academic Publishers (to appear, 2006)
Google, http://www.google.com
Yahoo! http://www.yahoo.com
IEEE Standard Upper Ontology Working Group (SUO WG), http://suo.ieee.org
Web Ontology Language (OWL), W3C, http://www.w3.org/2004/OWL/
OntoWeb, http://ontoweb.org
OpenCyc, http://www.opencyc.org/
Ontologies for Education, http://iiscs.wssu.edu/o4e/
Semantic Web Community Portal, http://www.semanticweb.org
KAON2 – Ontology Management for the Semantic Web, http://kaon2.semanticweb.org
Tan, M., Goh, A.: The Use of Ontologies in Web-based Learning. In: Proc. Workshop on Applications of Semantic Web Technologies for e-Learning, Hiroshima, Japan (2004)
Cho, Y.H., Kim, J.K.: Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in e-Commerce. Expert Systems with Applications 26(2), 233–246 (2004)
Cho, Y.H., Kim, J.K., Kim, S.H.: A Personalized Recommender System Based on Web Usage Mining and Decision Tree Induction. Expert Systems with Applications 23(3), 329–342 (2002)
Cho, Y.B., Cho, Y.H., Kim, S.H.: Mining Changes in Customer Buying Behavior for Collaborative Recommendations. Expert Systems with Applications 28(2), 359–369 (2005)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-based and Collaborative Filters in an Online Newspaper. In: Proc. ACM SIGIR Workshop on Recommender Systems (1999)
Kim, D., Yum, B.J.: Collaborative Filtering Based on Iterative Principal Component Analysis. Expert Systems with Applications 28(4), 823–830 (2005)
Kim, Y.S., Kim, B.J., Song, J., Kim, S.M.: Development of a Recommender System Based on Navigational and Behavioral Patterns of Customers in e-Commerce Sites. Expert Systems with Applications 28(2), 381–393 (2005)
Wang, F.H., Shao, H.M.: Effective Personalized Recommendation Based on Time-framed Navigational Clustering and Association Mining. Expert Systems with Applications 27(3), 365–377 (2004)
Wang, Y.F., Chuang, Y.L., Hsu, M.H., Keh, H.C.: A Personalized Recommender System for the Cosmetic Business. Expert Systems with Applications 26(3), 427–434 (2004)
Yu, L., Liu, L., Li, X.: A Hybrid Collaborative Filtering Method for Multiple-interests and Multiple-content Recommendation in e-Commerce. Expert Systems with Applications 28(1), 67–77 (2005)
Protégé-2000 – Ontology Editor, http://protege.stanford.edu
Hozo – Ontology Editor, http://www.hozo.jp
ATop – Topic Map Editor & Navigator, http://sourceforge.net/projects/atop
Ontopoly – Ontology-driven Topic Map Editor, http://www.ontopia.net
TM4L – Topic Map Editor and Browser, http://compsci.wssu.edu/iis/nsdl/
Baeza-Yates, R., Ribeiro-Neto, B. (eds.): Modern Information Retrieval. Addison Wesley, ACM Press (1999)
Sorensen, C., Yoo, Y., Lyytinen, K., DeGross, J.I. (eds.): Designing Ubiquitous Information Environments: Socio-Technical Issues and Challenges. IFIP. Springer, Heidelberg (2005)
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Chan, S.S.M., Jin, Q. (2006). Collaboratively Shared Information Retrieval Model for e-Learning. In: Liu, W., Li, Q., W.H. Lau, R. (eds) Advances in Web Based Learning – ICWL 2006. ICWL 2006. Lecture Notes in Computer Science, vol 4181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925293_12
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DOI: https://doi.org/10.1007/11925293_12
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