Interactive Exploration of Multi-Dimensional and Hierarchical Information Spaces with Real-Time Preference Elicitation

• Autorzy: Tzitzikas Y. , Papadakos P.
• Języki publikacji: EN

Abstrakty

EN

Current proposals for preference-based information access seem to ignore that users should be acquainted with the information space and the available choices for describing effectively their preferences. Furthermore users rarely formulate complex (preference or plain) queries. The interaction paradigm of Faceted Dynamic Taxonomies (FDT) allows users to explore an information space and to restrict their focus without having to formulate queries. Instead the users can restrict their focus (object set, or set of choices in general) gradually through a simple set of actions, each corresponding to a more refined query (formulated on-the-fly) which can be enacted by a simple click. In this paper we extend this interaction paradigm with actions that allow users to dynamically express their preferences. The proposed model supports progressive preference elicitation, inherited preferences and scope-based resolution of conflicts over single or multi-valued attributes with hierarchically organized values. Finally we elaborate on the algorithmic perspective and the applicability of the model over large information bases.

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 122, nr 4
• Strony: 357--399
• Opis fizyczny: Bibliogr. 56 poz., tab.

Twórcy

autor

Tzitzikas Y.

• Institute of Computer Science, FORTH-ICS, Greece, and Computer Science Department, University of Crete, Greece

autor

Papadakos P.

• Institute of Computer Science, FORTH-ICS, Greece, and Computer Science Department, University of Crete, Greece

Bibliografia

• [1] Special issue on Supporting Exploratory Search, Communications of the ACM, 49(4), April 2006.
• [2] Agrawal, R., Borgida, A., Jagadish, H.: Efficient management of transitive relationships in large data and knowledge bases, ACM SIGMOD Record, 18(2), 1989, 253–262.
• [3] Agrawal, R., Wimmers, E. L.: A framework for expressing and combining preferences, SIGMOD ’00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, ACM, New York, NY, USA, 2000, ISBN 1-58113-217-4.
• [4] Andreka, H., Ryan, M., Schobbens, P.-Y.: “Operators and Laws for Combining Preference Relations”, Journal of Logic and Computation, 12(1), 2002, 13–53.
• [5] Balke, W.-T., G¨untzer, U.: Multi-objective query processing for database systems, VLDB ’04: Proceedings of the Thirtieth international conference on Very large data bases, VLDB Endowment, 2004, ISBN 0-12-088469-0.
• [6] Barrett, R., Salles, M.: Social Choice With Fuzzy Preferences, Economics working paper archive (university of rennes 1 & university of caen), Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS, 2006.
• [7] Ben-Yitzhak, O., Golbandi, N., Har’El, N., Lempel, R., Neumann, A., Ofek-Koifman, S., Sheinwald, D., Shekita, E., Sznajder, B., Yogev, S.: Beyond basic faceted search, Proceedings of the international conference on Web search and web data mining, 2008, 33–44.
• [8] Bot, R. S., Wu, Y. B.: ”Improving Document Representations Using Relevance Feedback: The RFA Algorithm”, Procs of the 13th ACM Intern. Conf. on Information and Knowledge Management, Washington, USA, 2004.
• [9] Boutilier, C., Brafman, R. I., Domshlak, C., Hoos, H. H., Poole, D.: CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements, Journal Of Artificial Intelligence Research, 21, 2004, 135–191.
• [10] Chakrabarti, K., Chaudhuri, S., Hwang, S.: “Automatic Categorization of Query Results”, Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, 2004, 755–766.
• [11] Chen, L., Pu, P.: Survey of Preference Elicitation Methods, Technical report, Swiss Federal Institute of Technology in Lausanne (EPFL), 2004.
• [12] Choi, J., Kim, M., Raghavan, V. V.: ”Adaptive Feedback Methods in an Extended Boolean Model”, ACM SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, New Orleans, LA, September 2001.
• [13] Chomicki, J.: Preference formulas in relational queries, ACM Trans. Database Syst., 28(4), 2003, 427–466, ISSN 0362-5915.
• [14] desJardins, M., Eaton, E., Wagstaff, K. L.: Learning user preferences for sets of objects, ICML ’06: Proceedings of the 23rd international conference on Machine learning, ACM, New York, NY, USA, 2006, ISBN1-59593-383-2.
• [15] Fishburn, P.: Utility Theory for Decision Making, Wiley, New York, 1970.
• [16] Gadanho, S. C., Lhuillier, N.: Addressing uncertainty in implicit preferences, RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, ACM, New York, NY, USA, 2007, ISBN 978-1-59593-730–8.
• [17] Georgiadis, P., Kapantaidakis, I., Christophides, V., Nguer, E. M., Spyratos, N.: Efficient Rewriting Algorithms for Preference Queries, ICDE, 2008.
• [18] Hildebrand, M., van Ossenbruggen, J., Hardman, L.: “facet: A Browser for Heterogeneous Semantic Web Repositories”, Procs of ISWC ’06, Athens, GA, USA, Nov. 2006.
• [19] Hyvönen, E., M¨akel¨a, E., Salminen, M., Valo, A., Viljanen, K., Saarela, S., Junnila, M., Kettula, S.: ”Museum Finland – Finnish Museums on the Semantic Web”, Journal of Web Semantics, 3(2), 2005, 25.
• [20] Inan, H.: Search Analytics: A Guide to Analyzing and Optimizing Website Search Engines, Book Surge Publishing, 2006.
• [21] Kahn, A. B.: Topological sorting of large networks, Commun. ACM, 5(11), 1962, 558–562, ISSN 0001-0782.
• [22] Karlson, A. K., Robertson, G. G., Robbins, D. C., Czerwinski, M. P., Smith, G. R.: “FaThumb: a Facet-Based Interface for Mobile Search.”, Procs of the Conference on Human Factors in computing systems, CHI’06, New York, NY, USA, Apr. 2006.
• [23] Keeney, R. L., Raiffa, H.: “Decisions with Multiple Objectives: Preferences and Value Tradeoffs”, John Wiley & Sons, 1976.
• [24] Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography, SIGIR Forum, 37(2), 2003, 18–28, ISSN 0163-5840.
• [25] Kießling, W.: Foundations of preferences in database systems, VLDB’02: Procs of the 28th intern. conf. on Very Large Data Bases, VLDB Endowment, 2002.
• [26] Kießling, W., Kostler, G.: “Preference SQL - Design, Implementation, Experiences”, Procs of the 28th Intern. Conf. on Very Large Data Bases (VLDB), Hong Kong, China, 2005.
• [27] Kopidaki, S., Papadakos, P., Tzitzikas, Y.: STC+ and HSTC: Two Novel Online Results Clustering Methods for Web Searching, WISE’09: Procs of the 10th Intern. Conf. on Web Information Systems Engineering, October 2009.
• [28] Koren, J., Zhang, Y., Liu, X.: Personalized interactive faceted search, WWW’08: Procs of the 17th intern. conf. on World Wide Web, ACM, New York, NY, USA, 2008, ISBN 9781605580852.
• [29] Korfhage, R. R.: “Information Storage and Retrieval”, John Wiley & Sons, 1997.
• [30] Koutrika, G., Ioannidis, Y.: Personalized Queries under a Generalized Preference Model, ICDE ’05: Proceedings of the 21st International Conference on Data Engineering, IEEE Computer Society, Washington, DC, USA, 2005, ISBN 0-7695-2285-8.
• [31] Mäkelä, E., Hyvönen, E., Saarela, S.: Ontogator - A Semantic View-Based Search Engine Service for Web Applications, International Semantic Web Conference, Athens, GA, USA, Nov. 2006.
• [32] Mäkelä, E., Viljanen, K., Lindgren, P., Laukkanen, M., Hyvönen, E.: Semantic yellow page service discovery: The veturi portal, In poster paper at ISWC ’05, Nov. 2005.
• [33] Manolis, N., Tzitzikas, Y.: “Interactive Exploration of Fuzzy RDF Knowledge Bases”, Procs of the 8th Extended Semantic Web Conference (ESWC’11), Heraklion, Greece, 2011.
• [34] Oren, E., Delbru, R., Decker, S.: ”Extending Faceted Navigation for RDF Data”, Procs of ISWC ’06, Athens, GA, USA, Nov. 2006.
• [35] Papadakos, P., Armenatzoglou, N., Kopidaki, S., Tzitzikas, Y.: ”On Exploiting Static and Dynamically Mined Metadata for Exploratory Web Searching”, Knowl. Inf. Syst., 30(3), 2012, 493–525.
• [36] Papadakos, P., Kopidaki, S., Armenatzoglou, N., Tzitzikas, Y.: Exploratory web searching with dynamic taxonomies and results clustering, ECDL’09: Procs of the 13th European Conf. on Digital Libraries, Corfu, Greece, September 2009.
• [37] Papadakos, P., Tzitzikas, Y., Zafeiri, D.: ”An Interactive Exploratory System with Real-Time Preference Elicitation”, Proceedings of the 13th International Conference on Web Information Systems Engineering (WISE’12), November 2012.
• [38] Papadias, D., Ta, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems, ACM Trans. Database Syst., 30(1), 2005, 41–82, ISSN 0362-5915.
• [39] Peintner, B., Viappiani, P., Yorke-Smith, N.: Preferences in Interactive Systems: Technical Challenges and Case Studies, AI Magazine, 29(4), Winter 2008, 13–24.
• [40] Pu, P., Chwn, L.: User-Involved Preference Elicitation for Product Search and Recommender Systems, AI Magazine, 29(4), Winter 2008, 93–103.
• [41] Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., Mcnee, S. M., Konstan, J. A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems, IUI ’02: Proceedings of the 7th international conference on Intelligent user interfaces, ACM Press, New York, NY, USA, 2002, ISBN 1581134592.
• [42] Rochio, J.: “Relevance Feedback in Information Retrieval”, in: The SMART Retrieval System (G. Salton, Ed.), Prentice Hall, Englewood Cliffs, NJ, 1971, 313–323.
• [43] Rossi, F., Venable, K. B., Walsh, T.: Preferences in constraint satisfaction and optimization, AI Magazine, 28(4), 2008.
• [44] Roy, S. B., Wang, H., Das, G., Nambiar, U., Mohania, M.: Minimum-effort driven dynamic faceted search in structured databases, CIKM’08: Procs of the 17th ACM conf. on Information and knowledge management, New York, NY, USA, 2008, ISBN 978-1-59593-991-3.
• [45] Sacco, G.: Some Research Results in Dynamic Taxonomy and Faceted Search Systems, SIGIR’2006 Workshop on Faceted Search, 2006.
• [46] Sacco, G. M.: Analysis and Validation of Information Access Through Mono, Multidimensional and Dynamic Taxonomies, FQAS, 2006.
• [47] Sacco, G. M., (Editors), Y. T.: Dynamic Taxonomies and Faceted Search: Theory, Practice and Experience, Springer, 2009, ISBN 978-3-642-02358-3, ISBN = 978-3-642-02358-3.
• [48] Schafer, J. B., Konstan, J. A., Riedl, J.: E-Commerce Recommendation Applications, Data Min. Knowl. Discov., 5(1-2), 2001, 115–153, ISSN 1384-5810.
• [49] Schraefel, M., Karam, M., Zhao, S.: “mSpace: Interaction Design for User-Determined, Adaptable Domain Exploration in Hypermedia”, Procs of Workshop on Adaptive Hypermedia and Adaptive Web Based Systems, Nottingham, UK, Aug. 2003.
• [50] Spyratos, N., Sugibuchi, T., Yang, J.: “Personalizing Queries over Large Data Tables”, Procs of the 15th East-European Conference on Advances in Databases and Information System (ADBIS 2011), Vienna, Austria, September 2011.
• [51] Stefanidis, K., Drosou, M., Pitoura, E.: Per K: personalized keyword search in relational databases through preferences, EDBT, 2010.
• [52] Tvarožek, M., Barla, M., Frivolt, G., Tomša, M., Bieliková, M.: Improving Semantic Search Via Integrated Personalized Faceted and Visual Graph Navigation., SOFSEM, 4910, Springer, 2008, ISBN 978-3-540-77565-2.
• [53] Tvarožek, M., Bieliková, M.: Personalized Faceted Browsing for Digital Libraries, ECDL, 2007.
• [54] Tzitzikas, Y., Armenatzoglou, N., Papadakos, P.: FleXplorer: A Framework for Providing Faceted and Dynamic Taxonomy-based Information Exploration, Procs of FIND’2008 (at DEXA ’08), Torino, Italy, Sept. 3, 2008, ISSN 1529-4188, Doi=10.1109/DEXA.2007.4312888.
• [55] Tzitzikas, Y., Spyratos, N., Constantopoulos, P.: “Mediators over Taxonomy-based Information Sources”, VLDB Journal, 14(1), 2005, 112–136.
• [56] Yee, K., Swearingen, K., Li, K., Hearst, M.: “Faceted Metadata for Image Search and Browsing”, Proceedings of the SIGCHI conference on Human factors in computing systems, 2003, 401–408.