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International Conference on Web Information Systems and Technologies

WEBIST 2011: Web Information Systems and Technologies pp 192–206Cite as

A Multi-factor Tag-Based Personalized Search

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 101))

Abstract

With the growing amount of information available on the Web, the task of retrieving documents of interest has become increasingly difficult. Personalized search has got significant attention because it considers the user’s preference into the search process to improve the results. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this study, we investigate how a search engine can elicit users’ preferences by exploring the user’s tagging activity from various sources. We propose a simple yet flexible model to succinctly represent user preferences based on multiple factors. Our experiments show that users’ preferences can be elicited from a multi-factor tagging data and personalized search based on user preference yields significant precision improvements over the existing ranking mechanisms in the literature.

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References

  1. Au Yeung, C.M., Gibbins, N., Shadbolt, N.: A study of user profile generation from folksonomies. In: Proceedings of the Workshop on Social Web and Knowledge Management (SWKM 2008), Beijing, China, April 21-25, 2007, pp. 1–8 (2008)

    Google Scholar 

  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley (1999)

    Google Scholar 

  3. Bender, M., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Schenkel, R., Weikum, G.: Exploiting social relations for query expansion and result ranking. In: ICDE Workshops, pp. 501–506. IEEE Computer Society (2008)

    Google Scholar 

  4. Biancalana, C.: Social tagging for personalized web search. In: AI*IA 2009: Emergent Perspectives in Artificial Intelligence, pp. 232–242 (2009)

    Google Scholar 

  5. Boninsegna, M., Rossi, M.: Similarity measures in computer vision. Pattern Recognition Letters 15(12), 1255–1260 (1994)

    Article  Google Scholar 

  6. Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’el, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: CIKM 2009: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1227–1236. ACM, New York (2009)

    Google Scholar 

  7. Dou, Z., Song, R., Wen, J.-R.: A large-scale evaluation and analysis of personalized search strategies. In: WWW 2007: Proceedings of the 16th International Conference on World Wide Web, pp. 581–590. ACM, New York (2007)

    Google Scholar 

  8. Durao, F., Dolog, P.: A personalized Tag-Based recommendation in social web systems. In: Proceedings of International Workshop on Adaptation and Personalization for Web 2.0 (AP-WEB 2.0 2009) at UMAP 2009, vol. 485 (2009)

    Google Scholar 

  9. Ferragina, P., Gulli, A.: A personalized search engine based on web-snippet hierarchical clustering. Software: Practice and Experience 38(2), 189–225 (2008)

    Google Scholar 

  10. Gemmell, J., Shepitsen, A., Mobasher, M., Burke, R.: Personalization in folksonomies based on tag clustering. In: Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (2008)

    Google Scholar 

  11. Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW 2002: Proceedings of the 11th International Conference on World Wide Web, pp. 517–526. ACM, New York (2002)

    Google Scholar 

  12. Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies, pp. 506–514 (2007)

    Google Scholar 

  13. Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM, New York (2005)

    Chapter  Google Scholar 

  14. Liu, F., Yu, C., Member, S., Meng, W.: Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering 16, 28–40 (2004)

    Article  Google Scholar 

  15. Ma, Z., Pant, G., Sheng, O.R.L.: Interest-based personalized search. ACM Trans. Inf. Syst. 25(1), 5 (2007)

    Article  Google Scholar 

  16. Noll, M.G., Meinel, C.: Web Search Personalization Via Social Bookmarking and Tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Pretschner, A., Gauch, S.: Ontology based personalized search. In: 1999 Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 391–398 (1999)

    Google Scholar 

  18. Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW 2006: Proceedings of the 15th International Conference on World Wide Web, pp. 727–736. ACM, New York (2006)

    Google Scholar 

  19. Shen, X., Tan, B., Zhai, C.: Implicit user modeling for personalized search. In: CIKM 2005: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 824–831. ACM, New York (2005)

    Google Scholar 

  20. Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: CIKM 2007: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 525–534. ACM, New York (2007)

    Chapter  Google Scholar 

  21. Speretta, M., Gauch, S.: Personalized search based on user search histories. In: IEEE / WIC / ACM International Conference on Web Intelligence, pp. 622–628 (2005)

    Google Scholar 

  22. Tan, B., Shen, X., Zhai, C.: Mining long-term search history to improve search accuracy. In: KDD 2006: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 718–723. ACM, New York (2006)

    Google Scholar 

  23. Teevan, J., Dumais, S.T., Liebling, D.J.: To personalize or not to personalize: modeling queries with variation in user intent. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 163–170. ACM, New York (2008)

    Google Scholar 

  24. Teevan, J., Morris, M.R., Bush, S.: Discovering and using groups to improve personalized search. In: WSDM 2009: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 15–24. ACM, New York (2009)

    Google Scholar 

  25. Williams, G.: Linear Algebra With Applications. Jones and Bartlett Publishers, Inc., USA (2007)

    Google Scholar 

  26. Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–162. ACM, New York (2008)

    Google Scholar 

  27. Yang, B., Jeh, G.: Retroactive answering of search queries. In: WWW 2006: Proceedings of the 15th International Conference on World Wide Web, pp. 457–466. ACM, New York (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Intelligent Web and Information Systems, Aalborg University, Selma Lagerlöfs Vej 300, DK-9220, Aalborg-East, Denmark

    Frederico Durao, Ricardo Lage & Peter Dolog

  2. Science and Technology Institution Building, Istanbul Technical University, 34469, Maslak, Istanbul

    Nilay Coskun

Authors
  1. Frederico Durao
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  2. Ricardo Lage
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  3. Peter Dolog
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  4. Nilay Coskun
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Editor information

Editors and Affiliations

  1. Department of Systems and Informatics, Institute for Systems and Technologies of Information, Control and Communication (INSTICC), and, Instituto Politécnico de Setúbal (IPS), Rua do Vale de Chaves, Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe  & José Cordeiro  & 

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Durao, F., Lage, R., Dolog, P., Coskun, N. (2012). A Multi-factor Tag-Based Personalized Search. In: Filipe, J., Cordeiro, J. (eds) Web Information Systems and Technologies. WEBIST 2011. Lecture Notes in Business Information Processing, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28082-5_14

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  • DOI: https://doi.org/10.1007/978-3-642-28082-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28081-8

  • Online ISBN: 978-3-642-28082-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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