Abstract
The fundamental idea behind personalization is to first learn something about the users of a system, and then use this information to support their future activities. When effective algorithms can be developed to learn user preferences, and when the methods for supporting future actions are achievable, personalization can be very effective. However, personalization is difficult in domains where tracking users, learning their preferences, and affecting their future actions is not obvious. In this paper, we introduce a novel method for providing personalized re-ordering of Web search results, based on allowing the searcher to maintain distinct search topics. Search results viewed during the search process are monitored, allowing the system to automatically learn about the users’ current interests. The results of an evaluation study show improvements in the precision of the top 10 and 20 documents in the personalized search results after selecting as few as two relevant documents.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ahn, J., Brusiloviksy, P., He, D., Grady, J., Li, Q.: Personalized web exploration with task models. In: Proceedings of the World Wide Web Conference, pp. 1–10 (2008)
Buckley, C.: Why current IR engines fail. In: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 584–585 (2004)
He, D., Brusiloviksy, P., Grady, J., Li, Q., Ahn, J.: How up-to-date should it be? the value of instant profiling and adaptation in information filtering. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 699–705 (2007)
Hinkle, D.E., Wiersma, W., Jurs, S.G.: Applied Statistics for the Behavioural Sciences. Houghton Mifflin Company (1994)
Hoeber, O.: Exploring Web search results by visually specifying utility functions. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 650–654 (2007)
Hoeber, O.: User evaluation methods for visual Web search interfaces. In: Proceedings of the International Conference on Information Visualization (2009)
Jansen, B.J., Pooch, U.: A review of Web searching studies and a framework for future research. Journal of the American Society for Information Science and Technology 52(3), 235–246 (2001)
Kamvar, S., Mayer, M.: Personally speaking (2007), http://googleblog.blogspot.com/2007/02/personally-speaking.html
Kobayashi, M., Takeda, K.: Information retrieval on the Web. ACM Computing Surveys 32(2), 114–173 (2000)
Ma, Z., Pant, G., Sheng, O.R.L.: Interest-based personalized search. ACM Transactions on Information Systems 25(1) (2007)
Netscape: Open directory project (2008), http://www.dmoz.org/
Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.: Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction 13(4), 311–372 (2003)
Pirolli, P., Card, S.: Information foraging. Psychological Review 106(4), 643–675 (1999)
Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
van Rijsbergen, C.J.: Information Retrieval, Butterworths (1979)
Spink, A., Wolfram, D., Jansen, B.J., Saracevic, T.: Searching the Web: The public and their queries. Journal of the American Society for Information Science and Technology 52(3), 226–234 (2001)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web search based on user profile construction without any effort from users. In: Proceedings of the World Wide Web Conference, pp. 675–684 (2004)
Wedig, S., Madani, O.: A large-scale analysis of query logs for assessing personalization opportunities. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 742–747 (2006)
Yahoo: Yahoo! developer network: Yahoo! search Web services (2008), http://developer.yahoo.com/search
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hoeber, O., Massie, C. (2010). Automatic Topic Learning for Personalized Re-Ordering of Web Search Results. In: Snášel, V., Szczepaniak, P.S., Abraham, A., Kacprzyk, J. (eds) Advances in Intelligent Web Mastering - 2. Advances in Intelligent and Soft Computing, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10687-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-10687-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10686-6
Online ISBN: 978-3-642-10687-3
eBook Packages: EngineeringEngineering (R0)