Matrioshka's soft approaches to personalized web exploration

• Autorzy: Bordogna G. , Psaila G.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we present the soft approaches and techniques developed within Matrioshka, a prototypal meta-search system aimed at providing personalized exploratory facilities, addressing the well known Ranked-List Problem. Matrioshka implements a novel interaction framework that provides tools for clustering documents retrieved by search engines, tools for exploring the content of clusters through the analysis of some cluster properties, tools for generating disambiguated queries from the clusters, and tools for combining the clusters to highlight their shared contents. All these tools are defined based on soft operations, in order to deal with intrinsic semantic ambiguity, imprecision and uncertainty of complex web searches. In this way, the user is supported in the deployment of complex web search exploratory activities.

Słowa kluczowe

EN

Matrioshka; prototypal meta-search system; interaction framework

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2010
• Tom: Vol. 39, no 4
• Strony: 925--957
• Opis fizyczny: Bibliogr. 53 poz.

Twórcy

autor

Bordogna G.

autor

Psaila G.

• Via Pasubio 5, 1-24044 Dalmine (BG), Italy,

Bibliografia

• AGRAWAL, R., GOLLAPUDI, S., HALVERSON, A. and IEONG, S. (2009) Diversifying search results. In: Proceedings of ACM WSDM 2009, the Second International Conference on Web Search and Data Mining. ACM, New York, NY, 5-14.
• BALFE, E. and SMYTH, B. (2005) An Analysis of Query Similarity in Collaborative Web Search. In: Advances in Information Retrieval, 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain. LNCS 3408, Springer Verlag, 330-344.
• BORDOGNA, G., CAMPI, A., PSAILA, G. and RONCHI, S. (2008a) An interaction framework for mobile web search. In: Proceedings of MoMM-2009, the 6th International Conference on Advances in Mobile Computing and Multimedia. Linz, Austria. ACM, 183-191.
• BORDOGNA, G., CAMPI, A., PSAILA, G. and RONCHI, S. (2008b) A language for manipulating clustered web documents results. In: Proceeding of CIKM 2008, the 17th ACM conference on Information and knowledge management. Napa Valley, California. ACM, 23-32.
• BORDOGNA, G., CAMPI, A., PSAILA, G. and RONCHI, S. (2009a) A flexible language for exploring clusterd search results. In: A. Laurent and M.J. Lesot, eds., Scalable Fuzzy Algorithms for Data Management and Analysis. IGI Global, 179-213.
• BORDOGNA, G., CAMPI, A., PSAILA, G. and RONCHI, S. (2009b) Query Disambiguation Based on Novelty and Similarity Users Feedback. In: Proceeding of FQAS 2009, the 8th International Conference on Flexible Querying Answering Systems. Roskilde, Denmark. Springer Verlag, 179-190.
• BRIN, S. and PAGE, L. (1998) The anatomy of a large-scale hypertextual Web search engine. In: WWW-7: Proceedings of the seventh international conference on World Wide Web. Brisbane, Australia, 107-117.
• CARBONELL, J. and GOLDSTEIN, J. (1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of ACM SIGIR 1998, the 21st Annual International Conference on Research and Development in Information Retrieval. Melbourne, Australia. ACM, 335-336.
• CARD, S.K., MACKINLAY, J.D. and SHNEIDERMAN, B. (1999) Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc. San Francisco, CA.
• CARPINETO, C., OSINSKI, S., ROMANO, G. and WEISS, D. (2009) A Survey of Web Clustering Engines. ACM Computing Survey 41 (3).
• CHEN, H. and DUMAIS, S. (2000) Bringing Order to the Web: Automatically Categorizing Search Results. In: Proceedings of ACM CHI 2000, the International Conference on Human Factors in Computing Systems. The Hague, The Netherlands. ACM, 145-152.
• CHIRITA, P., FIRAN, C. and NEJDL, W. (2007) Personalized query expansion for the web. In: Proceedings of ACM SIGIR 2007, the 30th Annual International Conference on Research and Development in Information Retrieval. Amsterdam, The Netherlands. ACM, 7-14.
• CHUNG, W., CHEN, H. and NUNAMAKER, J.J. (2003) Business intelligence explorer: a knowledge map framework for discovering business intelligence on the Web. In: Proceedings of HICSS 2003 the 36th Annual Hawaii In-ternational Conference on System Sciences, vol. 1, p. 10.2, Big Island, Hawaii, USA.
• COATES, T., CONNOLLY, D., DACK, D., DAIGLE, L., DENENBERG, R. DURST, P.G.,HAWKE, S.,!ANNELLA, R., KLYNE, G., MASINTER, L., MEALLING, M., NEEDLEMAN, M. and WALSH, N. (2001) URIs, URLs, and URNs: Clarifications and recommendations 1.0. Technical report, World Wide Web Consortium, URI Planning Interest Group W3C/IETF, http://www.w3.org/TR/2001/NOTE-uri-clarification-20010921/.
• CUTTING, D., KARGER, D., PEDERSEN, J. and TUKEY, J. (1992) Scatter/ Gather: a cluster-based approach to browsing large document collections. In: Proceedings of SIGIR 1992, the 15th Annual International Conference on Research and Development in Information Retrieval. Copenhagen, Denmark. ACM, 318-329.
• DUBOIS, D. and PRADE, H. (1985) A Review of Fuzzy Set Aggregation Connectives. Information Sciences 36 (1-2), 85-121.
• FELLBAUM, C., ed. (1998) WordNet An Electronic Lexical Database. The MIT Press, Cambridge, MA, USA.
• FREYNE, J., SMYTH, B., COYLE, M., BALFE, E. and BRIGGS, P. (2004) Further Experiments on Collaborative Ranking in Community-Based Web Search. Artificial Intelligence Reviews 21 (3-4), 229-252.
• GOLLAPUDI, S. and SHARMA, A. (2009) An axiomatic approach for result diversification. In: Proceedings of WWW 2009, the 18th World Wide Web Conference. Madrid, Spain, 381-390.
• HEARST, M.A. and PEDERSON, J.O. (1996) Reexamining the cluster hypothesis: Scatter/gather on retrieval results. In: Proceedings of ACM SIGIR 1996, the Annual International Conference on Research and Development in Information Retrieval. Zurich, Switzerland. ACM.
• JANSEN, B., BOOTH, D. and SPINK, A. (2009) Patterns of query reformulation during Web searching. JASIST Journal of the American Society for Information Science and Technology 60 (7), 1358-1371.
• JANSEN, B. and SPINK, A. (2006) How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing and Management 42 (7), 248-263.
• JANSEN, B., SPINK, A. and SARACEVIC, T. (2000) Real life, real users and real needs: A study and analysis of users queries on the Web. Information Processing and Management 36 (2), 207-227.
• KAMPANYA, N., SHEN, R., KIM, S., NORTH, C. and Fox, E.A. (2004) Citiviz: A Visual User Interface to the CITIDEL System. In: Proceedings of ECDL 2004, the 8th European Conference on Research and Advanced Technology for Digital Libraries. LNCS 3232, Bath, UK. Springer Verlag, 122-133.
• LAD, A. and YANG, Y. (2007) Generalizing from Relevance Feedback using Named Entity Wildcards. In: Proceedings of ACM CIKM 2007, the Sixteenth Conference on Information and Knowledge Management. Lisbon, Portugal. ACM, 721-730.
• LAWRENCE, S. (2000) Context in Web Search. IEEE Data Engineering Bulletin 23 (3), 25-32.
• LEOUSKI, A. and CROFT, W. (1996) An Evaluation of Techniques for Clustering Search Results. Technical Report of the Department of Computer Science of University of Massachusetts at Amherst. IR-76, 122-133.
• LIU, F., YU, C. and MENG, W. (2004) Personalized Web Search For Improving Retrieval Effectiveness. IEEE Transactions on Knowledge and Data Engineering 16 (1), 28-40.
• LIU, Y., JIN, R. and CHAI, J. (2006) A statistical framework for query translation disambiguation. A CM Transactions on Asian Language Information Processing (TALIP) 5 (4), 360-387.
• LUCA, E.D. and NURNBERGER, A. (2005) A Meta Search Engine for User Adaptive Information Retrieval Interfaces for Desktop and Mobile Devices. In: Proceedings of PI A 2005, the 1st International Workshop on New Technologies for Personalized Information Access, in conjunction with UM05, the 10th International Conference on User Modeling. Edinburgh, UK, 23-34.
• MA, Z., PANT, G. arid SHENG, O. (2007) Interest-based personalized search. ACM Transactions on Information Systems 25 (1).
• MIHALKOVA, L. and MOONEY, R. (2009) Learning to Disambiguate Search Queries from Short Sessions. In: Proceedings of ECML-PKDD 2009, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases: Part II. Bled, Slovenia. Springer Verlag, 111-127.
• NOTESS, G., ED. (2006) Teaching Web Search. Information Today Inc., New Jersey, USA.
• OSINSKI, S. (2003) An Algorithm for Clustering of Web Search Results. Master thesis, Department of Computing Science, Poznan University of Technology, http://project.carrot2.org/publications/osinski-2003-lingo.pdf.
• PATWARDHAN, S., BANERJEE, S. and PEDERSEN, T. (2005) SenseRelate:: TargetWord: a generalized framework for word sense disambiguation. In: Proceedings of ACL 2005, the International Conference of the Association for Computational Linguistic, Interactive Poster and Demonstration Sessions. Ann Arbor, Michigan. Association for Computational Linguistics, 73-76.
• RADLINSKI, F., BENNETT, P.N., CARTERETTE, B. and JOACHIMS, T. (2009) Redundancy, diversity and interdependent document relevance. SIGIR Forum 43 (2), 46-52.
• ROUSSINOV, D. and CHEN, H. (2001) Information navigation on the web by clustering and summarizing query results. Information Processing and Management 37 (6), 789-816.
• SAMEH, A. and KADRAY, A. (2010) Semantic Web Search Results Clustering Using Lingo and WordNet. International Journal of Research and Reviews in Computer Science 1 (2), 71-76.
• SANDERSON, M. (2008) Ambiguous queries: test collections need more sense. In: Proceedings of ACM SIGIR 2008, the 31st Annual International Conference on Research and Development in Information Retrieval. Singapore. ACM, Singapore, 499-506.
• SEBRECHTS, M.M., VASILAKIS, J., MILLER, M.S. and J.V. CUGINI, S.J.L. (1999) Visualization of Search Results: A Comparative Evaluation of Text, 2D, and 3D Interfaces. In: Proceedings of ACM SIGIR 1999, the 22nd Annual International Conference on Research and Development in Information Retrieval. Berkeley, CA, USA. ACM, 3-10.
• SHEN, X., TAN, B. and ZHAI, C. (2005) Implicit user modeling for personalized search. In: Proceedings of ACM CIKM 2005, the 14th International Conference on Information and Knowledge Management. Bremen, Germany. ACM, 824-831.
• STALEY, E. and TWIDALE, M. (2000) Graphical interfaces to support information search. Technical report, University of Illinois, http://people.lis.uiuc.edu/ twidale/irinterfaces/bib-main.html.
• SUGIYAMA, K., HATANO, K. and YOSHIKAWA, M. (2004) Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of WWW 2004, the 13th International Conference on World Wide Web. New York, NY, USA. ACM, 675-684.
• SUN, J., ZENG, H., LIU, H., LU, Y. and CHEN, Z. (2005) CubeSVD: a novel approach to personalized Web search. In: Proceedings of WWW 2005, the 14th International Conference on World Wide Web. Chiba, Japan. ACM, 382-390.
• SWEENEY, S., CRESTANI, F. and LOSADA, D.E. (2008) Show me more’: Incremental length summarisation using novelty detection. Information Processing and Management 44 (2), 663-686.
• TEEVAN, J., DUMAIS, S. and LIEBLING, D. (2008) To personalize or not to personalize: modeling queries with variation in user intent. In: Proceedings of ACM SIGIR 2008, the 31st Annual International Conference on Research and Development in Information Retrieval, Singapore. Singapore. ACM, 163-170.
• VOORHEES, E. (1993) Using WordNet to disambiguate word senses for text retrieval. In: Proceedings of ACM SIGIR 1993, the 16th Annual Interna-tional Conference on Research and Development in Information Retrieval. Pittsburgh, PA, United States. ACM, 171-180.
• XU, Y. and CHEN, Z. (2006) Relevance judgment: What do information users consider beyond topicality? JASIST Journal of the American Society for Information Science and Technology 57 (7), 961-973.
• ZADEH, L. (1965) Fuzzy Sets. Information and Control 8, 338-353.
• ZAMIR, O. and ETZIONI, O. (1999) Grouper: a dynamic clustering interface to Web search results. Computer Networks 31 (11-16), 1361-1374.
• ZHAI, C., COHEN, W. and LAFPERTY, J. (2003) Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: Proceedings of ACM SIGIR 2003, the 26th Annual International Conference on Research and Development in Information Retrieval. Toronto, Canada. ACM, 10-17.
• ZHANG, B., LI, H., LIU, Y., JI, L., XI, W., FAN, W., CHEN, Z. and MA, W. (2005) Improving web search results using affinity graph. In: Proceedings of ACM SIGIR 2005, the 28th Annual International Conference on Research and Development in Information Retrieval. Salvador, Brazil. ACM, 504-511.
• ZHANG, Y. (2008) Complex adaptive filtering user profile using graphical models. Information Processing and Management 44 (6), 1886-1900.