Abstract
User relevance feedback plays an important role in the development of efficient and successful business strategies for several online domains such as: modeling user preferences for information retrieval, personalized recommender systems, automatic categorization of emails, online advertising, online auctions, etc. To achieve success, the business models should have some kind of interactive interface to receive user feedback and also a mechanism for user relevance feedback analyis to extract relevant information from large information repositories such as WWW. We present a rough set based discernibility approach to expand the user preferences by including the relevant conceptual terms extracted from the collection of documents rated by the users. In addition, a rough membership based ranking methodology is proposed to filter out the irrelevant documents retrieved from the information repositories, using an extended set of conceptual terms. This paper provides a detailed implementation of the proposed approach as well as its advantages in the context of user relevance feedback analysis based text information retrieval.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altavista (1995), http://www.altavista.com
Allan, J., Ballesteros, L., et al.: Recent Experiments with INQUERY. In: Proceedings of the Fourth Text Retrieval Conference (TREC-4), NIST Special Publication, pp. 49–63 (1995)
Anagnostopoulos, I., Anagnostopoulos, C., Vergados, D.D., Maglogiannis, I.: BRWM: A Relevance Feedback Mechanism for Webpage Clustering. AIAI, 44–52 (2006)
Brauen T.L., Holt R.C., et al.: Document Indexing Based on Relevance Feedback. Report ISR 14 to the National Science Foundation, Section XI, Department of Computer Science. Cornell University, Ithaca, NY (1968)
Bao, Y., Aoyama, S., Yamada, K., Ishii, N., Du, X.: A Rough Set Based Hybrid Method to Text Categorization. In: Second International Conference on Web Information Systems Engineering (WISE 2001), vol. 1, p. 294 (2001)
Belkin, N.J., Cool, C., et al.: Rutgers TREC 2001 Interactive Track Experience. In: The Tenth Text Retrieval Conference (TREC 2001) (2001)
Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10(1), 57–78 (1993)
Crestani, F.: Learning Strategies for an Adaptive Information Retrieval System using Neural Networks. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA (1993)
Croft, W.B.: Effective Text Retrieval Based on Combining Evidence from the Corpus and Users. IEEE Expert: Intelligent Systems and Their Applications 10(6), 59–63 (1995)
Chouchoulas, A., Shen, Q.: Rough Set-Aided Keyword Reduction for Text Categorisation. J of Applied Artificial Intelligence 15(9), 843–873 (2001)
Das-Gupta, P.: Rough Sets and Information Retrieval. In: Proc. of the Eleventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Set Oriented Models, pp. 567–581 (1988)
Deerwester, S., Dumais, S., et al.: Indexing by Latent Semantic Analysis. J. of the American Society for Information Science 41(6), 391–407 (1990)
Fuhr, N.: Two Models of Retrieval with Probabilistic Indexing. In: Proceedings of 1986 ACM conference on Research and development in information retrieval, Palazzo dei Congressi, Pisa, Italy, pp. 249–257 (1986)
Fuhr, N., Buckley, C.: A Probabilistic Learning Approach for Document Indexing. ACM Transactions on Information Systems (TOIS) 9(3), 223–248 (1991)
Fuhr, N., Buckley, C.: Optimizing Document Indexing and Search Term Weighting Based on Probabilistic Models. In: The First Retrieval Conference (TREC-1) NIST Special Publication, pp. 89–99 (1993)
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)
Google (1998), http://www.google.com
Gauch, S.J., Wang, et al.: A Corpus Analysis Approach for Automatic Query Expansion and its Extension to Multiple Databases. ACM Transactions on Information Systems 17(3), 250–269 (1999)
Glöckner, I., Knoll, A.: Natural Language Navigation in Multimedia Archives: An Integrated Approach. In: Proceedings of the seventh ACM international conference on Multimedia (Part 1), Orlando, Florida, United States, pp. 312–322 (1999)
Intarka Inc.: ProspectMiner (2000), http://www.intarka.com
Jochem H., Ralph B., Frank W.: WebPlan: Dynamic Planning for Domain Specific Search in the Internet (1999), http://wwwagr.informatik.uni-kl.de/~webplan/PAPER/Paper.html
Jensen, R., Shen, Q.: A Rough Set-Aided system for sorting WWW Bookmarks. In: Proc. 1st Asia-Pacific Conference, Web Intelligence, pp. 95–105 (2001)
Jolliffe I.T.: Principal Component Analysis. Springer Series in Statistics (2002)
Koenemann, J., Belkin, N.J.: A Case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness. In: Conference proceedings on Human factors in computing systems, Vancouver, Canada, pp. 205–212 (1996)
Korfhage, R.R.: Information Storage and Retrieval, pp. 221–232. Wiley Computer Publishing, Chichester (1997)
Komorowski J., Polkowski L., Andrzej S.: Rough Sets: A Tutorial (1999), http://www.let.uu.nl/esslli/Courses/skowron/skowron.ps
Kim, D.W., Lee, K.H.: A New Fuzzy Information Retrieval System Based on User Preference Model. In: The 10th IEEE International Conference on Fuzzy Systems (2001)
Losee, R.M.: Parameter Estimation for Probabilistic Document-Retrieval Models. J. of the American Society for Information Science 39(1), 8–16 (1988)
Losee, R.M., Bookstein, A.: Integrating Boolean Queries in Conjunctive Normal Form with Probabilistic Retrieval Models. Information Processing and Management 24(3), 315–321 (1988)
Lee, J.H.: Properties of Extended Boolean Models in information Retrieval. In: ACM Annual Conference on Research and Development in Information Retrieval, pp. 182–190 (1994)
Maron, M.E., Kuhns, J.L.: On Relevance, Probabilistic Indexing and Information Retrieval. J. of ACM 7(3), 216–244 (1960)
Mitchel, T.: Machine Learning. McGraw-Hill, New York, USA (1997)
Miyamoto, S.: Application of Rough Sets to Information Retrieval. JASIS 49(3), 195–205 (1998)
Menasalvas, E., Millan, S., Hochsztain, E.: A Granular Approach for Analyzing the Degree of Afability of a Website. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475. Springer, Heidelberg (2002)
Meng, X., Chen, Z.: Intelligent Web Search Through Adaptive Learning from Relevance Feedback. In: Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology, pp. 3060–3065. Idea Group Publishing (2005)
Pawlak, Z.: Rough Sets. Int. J. of Computer and Information Sciences 11(5), 341–356 (1982)
Pazzani, M., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying Interesting Web Sites. In: Proc. of the National Conference on Artificial Intelligence, Portland, OR, USA (1996)
Piccinelli, G., Mont, M.C.: Fuzzy-Set Based Information Retrieval for Advanced Help Desk. HP Laboratories, Bristol, UK (1998)
Pal, S.K., Skowron, A.: Rough Fuzzy Hybridization- A New Trend in Decision-making. Springer, U.S.A. (1999)
Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: G. Salton - The SMART Retrieval system: Experiments in automatic document processing, pp. 313–323. Prentice Hall, Englewood Cliffs (1971)
Roldano, C.: UserProfiling with Bayesian Belief Networks (1999), http://www.labs.bt.com/profsoc/facts/workshop/abstract/BBN.html
Salton, G.: The SMART Retrieval System: Relevance Feedback and the Optimization of Retrieval Effectiveness. Prentice Hall, Englewood Cliffs, USA (1971)
Salton, G., Fox, E.A., et al.: Advanced Feedback Methods in Information Retrieval. J. of the American Society for Information Science 36(3), 200–210 (1985)
Salton, G., Buckley, C.: Improving Retrieval Performance by Relevance Feedback. J of the American Society for Information Science 41(4), 288–297 (1990)
Subtil, P., Mouaddib, N., et al.: A fuzzy information retrieval and management system and its applications. In: Proceedings of the 1996 ACM symposium on Applied Computing, Philadelphia, Pennsylvania, United States, pp. 537–541 (1996)
Stefanowski, J., Tsoukias, A.: Incomplete Information Tables and Rough Classification. Computational Intelligence 17, 454–466 (2001)
Srinivasan, P., Ruiz, M.E., Kraft, D.H., Chen, J.: Vocabulary Mining for Information Retrieval: Rough Sets and Fuzzy Sets. Information Processing and Management 37, 15–38 (2001)
Szczepaniak, P.S., Niewiadimski, A.: Internet Search Based on text intuitionistic Fuzzy Similarity. In: Intelligent Exploration of the Web, pp. 96–102. Physica –Verlag, Springer, Heidelberg, New York (2003)
Szczepaniak, P.S., Gil, M.: Practical Evaluation of textual fuzzy Similarity as a Tool for Information retrieval. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds.) AWIC 2003. LNCS (LNAI), vol. 2663, pp. 250–257. Springer, Heidelberg (2003)
Singh, S., Dey, L.: A new Customized Document Categorization scheme using Rough Membership. Int. J. of Applied Soft-Computing, 373–390 (2005)
Shtykh, R.Y., Jin, O.: Enhancing IR with User-Centric Integrated Approach of Interest Change Driven Layered Profiling and User Contributions. In: 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW 2007), pp. 240–245 (2007)
Verhoeff, J., Goffman, W., et al.: Inefficiency of the use of Boolean Functions for Information Retrieval Systems. CACM 4(12), 557–594 (1961)
Wong, S.K.M., Yao, Y.Y.: On Modeling Information Retrieval with Probabilistic Inference. ACM Transactions on Information Systems 13(1), 38–68 (1995)
Widyantoro, D.H., Ioerger, T.R., et al.: Learning User Interest Dynamics with a Three Descriptor Representation. J. of the American Society for Information Science and Technology 52(3), 212–225 (2001)
Wilson, M.L., Schraefel, M.C., White, R.W.: Evaluating Advanced Search Interfaces using Established Information-Seeking Models. J. of the American Society for Information Science and Technology (Submitted 2007)
Web 2.0 Business Models (2007), http://www.deitel.com/eBook/Web20BusinessModels/tabid/2498/Default.aspx
Yahoo! (1994), http://www.yahoo.com
Zadeh, L.A.: Fuzzy Sets. Inform. and Control 8, 338–365 (1965)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Singh, S., Prasad, B. (2008). User Relevance Feedback Analysis in Text Information Retrieval: A Rough Set Approach. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-79005-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79004-4
Online ISBN: 978-3-540-79005-1
eBook Packages: EngineeringEngineering (R0)