Personalizing search via automated analysis of interests and activities

ABSTRACT

We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.

REFERENCES

• 1: Adler, L. M. (1957). A modification of Kendall's tau for the case of arbitrary ties in both rankings. Journal of the American Statistical Society, 52: 33--35.
• 2: Anick, P. (2004). Using terminological feedback for Web search refinement: a log-based study. In Proceedings of WWW '04, 89--95.
• 3: Krishna Bharat, SearchPad: explicit capture of search context to support Web search, Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking, p.493-501, June 2000, Amsterdam, The Netherlands
• 4: Budzik, J. and Hammond, K. (1999). Watson: Anticipating and contextualizing information needs. In Proceedings of ASISIT '99, 727--740.
• 5: Susan Dumais , Edward Cutrell , JJ Cadiz , Gavin Jancke , Raman Sarin , Daniel C. Robbins, Stuff I've seen: a system for personal information retrieval and re-use, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860451]
• 6: Susan Gauch , Jeason Chaffee , Alaxander Pretschner, Ontology-based personalized search and browsing, Web Intelligence and Agent System, v.1 n.3-4, p.219-234, March 2003
• 7: Google Personal. http://labs.google.com/personalized
• 8: Hawking, D. and Craswell, N. (2001). Overview of the TREC-2001 Web Track. In Proceedings of TREC '01, 61--68.
• 9: Kalervo Järvelin , Jaana Kekäläinen, IR evaluation methods for retrieving highly relevant documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.41-48, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345545]
• 10: Glen Jeh , Jennifer Widom, Scaling personalized web search, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775191]
• 11: Diane Kelly , Jaime Teevan, Implicit feedback for inferring user preference: a bibliography, ACM SIGIR Forum, v.37 n.2, p.18-28, Fall 2003 [doi>10.1145/959258.959260]
• 12: Jürgen Koenemann , Nicholas J. Belkin, A case for interaction: a study of interactive information retrieval behavior and effectiveness, Proceedings of the SIGCHI conference on Human factors in computing systems: common ground, p.205-212, April 13-18, 1996, Vancouver, British Columbia, Canada [doi>10.1145/238386.238487]
• 13: Kritikopoulos, A. and Sideri, M. (2003). The Compass Filter: Search engine result personalization using Web communities. In Proceedings of ITWP.
• 14: Fang Liu , Clement Yu , Weiyi Meng, Personalized web search by mapping user queries to categories, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA [doi>10.1145/584792.584884]
• 15: Kathleen R. McKeown , Noemie Elhadad , Vasileios Hatzivassiloglou, Leveraging a common representation for personalized search and summarization in a medical digital library, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, May 27-31, 2003, Houston, Texas
• 16: Masahiro Morita , Yoichi Shinoda, Information filtering based on user behavior analysis and best match text retrieval, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.272-281, July 03-06, 1994, Dublin, Ireland
• 17: Nielsen, J. Personalization is overrated. In Jakob Nielsen's Alertbox for October 4, 1998. http://www.useit.com/alertbox/981004.html.
• 18: James Pitkow , Hinrich Schütze , Todd Cass , Rob Cooley , Don Turnbull , Andy Edmonds , Eytan Adar , Thomas Breuel, Personalized search, Communications of the ACM, v.45 n.9, September 2002 [doi>10.1145/567498.567526]
• 19: Ian Ruthven , Mounia Lalmas, A survey on the use of relevance feedback for information access systems, The Knowledge Engineering Review, v.18 n.2, p.95-145, June 2003 [doi>10.1017/S0269888903000638]
• 20: Xuehua Shen , Cheng Xiang Zhai, Exploiting query history for document ranking in interactive information retrieval, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860509]
• 21: Sparck Jones, K., Walker, S. and Robertson, S. A. (1998). Probabilistic model of information retrieval: Development and status. Technical Report TR-446, Cambridge University Computer Laboratory.
• 22: Speretta, M. and Gauch, S. (2004). Personalizing search based on user search history. Submitted to CIKM '04. http://www.ittc.ku.edu/keyconcept/
• 23: Kazunari Sugiyama , Kenji Hatano , Masatoshi Yoshikawa, Adaptive web search based on user profile constructed without any effort from users, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988764]
• 24: Teevan, J. (2005). The Re:Search Engine: Helping people return to information on the Web. To appear in Proceedings of SIGIR '05 (Doctoral Consortium).
• 25: Jaime Teevan , Christine Alvarado , Mark S. Ackerman , David R. Karger, The perfect search engine is not enough: a study of orienteering behavior in directed search, Proceedings of the SIGCHI conference on Human factors in computing systems, p.415-422, April 24-29, 2004, Vienna, Austria [doi>10.1145/985692.985745]
• 26: Teevan, J., Dumais, S. T. and Horvitz, E. (2005). Beyond the commons: Investigating the value of personalizing Web search. In Proceedings of the Workshop on New Technologies for Personalized Information Access (PIA).

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

General Terms:
Algorithms, Design, Experimentation, Human Factors, Languages, Measurement

Keywords:
adaptive interfaces, personalized search, web search tools