Enhancing web search by promoting multiple search engine use

ABSTRACT

Any given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users' best interest to utilize multiple search engines. In this paper, we propose and evaluate a framework that maximizes users' search effective-ness by directing them to the engine that yields the best results for the current query. In contrast to prior work on meta-search, we do not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. We describe a machine learning approach to supporting switching between search engines and demonstrate its viability at tolerable interruption levels. Our findings have implications for fluid competition between search engines.

REFERENCES

• 1: Eugene Agichtein , Eric Brill , Susan Dumais, Improving web search ranking by incorporating user behavior information, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148177]
• 2: Buckley, C. & Walz, J. (1999). Overview of the TREC-8 query track. In Voorhees, E.M. & Harman, D.K. (Eds.) Proc. TREC-8, 65--76.
• 3: Jay Budzik , Kristian J. Hammond, User interactions with everyday applications as context for just-in-time information access, Proceedings of the 5th international conference on Intelligent user interfaces, p.44-51, January 09-12, 2000, New Orleans, Louisiana, United States [doi>10.1145/325737.325776]
• 4: James P. Callan , Zhihong Lu , W. Bruce Croft, Searching distributed collections with inference networks, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.21-28, July 09-13, 1995, Seattle, Washington, United States [doi>10.1145/215206.215328]
• 5: Charles L. A. Clarke , Eugene Agichtein , Susan Dumais , Ryen W. White, The influence of caption features on clickthrough patterns in web search, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277767]
• 6: Michael Collins, Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.1-8, July 06, 2002 [doi>10.3115/1118693.1118694]
• 7: Craswell, N., Hawking, D. & Thistlewaite, P. (1999). Merging results from isolated search engines. In Proc. Australasian Database Conference, 189--200.
• 8: Steve Cronen-Townsend , Yun Zhou , W. Bruce Croft, Predicting query performance, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564429]
• 9: Mary Czerwinski , Eric Horvitz , Susan Wilhite, A diary study of task switching and interruptions, Proceedings of the SIGCHI conference on Human factors in computing systems, p.175-182, April 24-29, 2004, Vienna, Austria [doi>10.1145/985692.985715]
• 10: Michael Gordon , Praveen Pathak, Finding information on the World Wide Web: the retrieval effectiveness of search engines, Information Processing and Management: an International Journal, v.35 n.2, p.141-180, March 1999 [doi>10.1016/S0306-4573(98)00041-7]
• 11: Harman, D. (1993). Overview of the first text retrieval conference. In Proc. SIGIR, 36--47.
• 12: David Hawking , Nick Craswell , Peter Bailey , Kathleen Griffihs, Measuring Search Engine Quality, Information Retrieval, v.4 n.1, p.33-59, April 2001 [doi>10.1023/A:1011468107287]
• 13: Allison P. Heath , Ryen W. White, Defection detection: predicting search engine switching, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367712]
• 14: Eric Horvitz , Johnson Apacible, Learning and reasoning about interruption, Proceedings of the 5th international conference on Multimodal interfaces, November 05-07, 2003, Vancouver, British Columbia, Canada [doi>10.1145/958432.958440]
• 15: 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]
• 16: Yun-Fang Juan , Chi-Chao Chang, An analysis of search engine switching behavior using click streams, Special interest tracks and posters of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1062745.1062863]
• 17: Jure Leskovec , Susan Dumais , Eric Horvitz, Web projections: learning from contextual subgraphs of the web, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242636]
• 18: Tridas Mukhopadhyay , Uday Rajan , Rahul Telang, Competition between Internet Search Engines, Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 8, p.80216.1, January 05-08, 2004
• 19: Pew Internet & American Life Project. (2005). Search Engine Users. Accessed October 16, 2007. Available at: http://www.pewinternet.org/pdfs/PIP_Searchengine_users.pdf
• 20: James Pitkow , Peter Pirolli, Mining longest repeating subsequences to predict world wide web surfing, Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems, p.13-13, October 11-14, 1999, Boulder, Colorado
• 21: Yves Rasolofo , Faïza Abbaci , Jacques Savoy, Approaches to collection selection and results merging for distributed information retrieval, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502618]
• 22: Rorvig, M. (2000). A new method of measurement for question difficulty. In Proc. ASIS, 372--378.
• 23: Luo Si , Jamie Callan, A semisupervised learning method to merge search engine results, ACM Transactions on Information Systems (TOIS), v.21 n.4, p.457-491, October 2003 [doi>10.1145/944012.944017]
• 24: Telang, R., Mukhopadhyay, T. & Wilcox, R. (1999). An empirical analysis of the antecedents of internet search engine choice. In Proc. Workshop on Information Systems and Economics (WISE, Charlotte NC).
• 25: Andrew H. Turpin , William Hersh, Why batch and user evaluations do not give the same results, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.225-231, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383992]
• 26: Ryen W. White , Steven M. Drucker, Investigating behavioral variability in web search, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242576]
• 27: Elad Yom-Tov , Shai Fine , David Carmel , Adam Darlow, Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076121]

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

General Terms:
Experimentation, Human Factors, Measurement

Keywords:
search engine switching