ABSTRACT
Many search systems provide users with recommended queries during online information seeking. Although usage statistics are often used to recommend queries, this information is usually not displayed to the user. In this study, we investigate how the presentation of this information impacts use of query suggestions. Twenty-three subjects used an experimental search system to find documents about four topics. Eight query suggestions were provided for each topic: four were high quality queries and four were low quality queries. Fake usage information indicating how many other people used the queries was also provided. For half the queries this information was high and for the other half this information was low. Results showed that subjects could distinguish between high and low quality queries and were not influenced by the usage information. Qualitative data revealed that subjects felt favorable about the suggestions, but the usage information was less important for the search task used in this study.
- Banerjee, A.V. A simple model of herd behavior. Quarterly Journal of Economics 107, 3 (1992), 797--817.Google ScholarCross Ref
- Baron, R. S., Vandello, J. A. & Brunsman, B. The forgotten variable in conformity research: Impact of task importance on social influence. Journal of Personality and Social Psychology 71, 5 (1996), 915--927.Google ScholarCross Ref
- Bates, M. J. Idea tactics. Journal of the American Society for Information Science, 30 (1979), 280--289.Google ScholarCross Ref
- Belkin, N. J., Cool, C., Stein, A., & Thiel, U. Cases, scripts, and information-seeking strategies: on the design of interactive information retrieval systems. Expert Systems with Applications 9, 3 (1995), 379--395.Google ScholarCross Ref
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom and cultural change as informational cascades. Journal of Political Economy 100, 5 (1992), 992--1026.Google Scholar
- Chen, Y.-F. Herd behavior in purchasing books online. Computers in Human Behavior 24, (2008), 1977--1992. Google ScholarDigital Library
- Cialdini, R. B. Influence: Science and practice (4th ed.), Boston, MA: Allyn & Bacon, 2001.Google Scholar
- Cialdini, R. B., & Goldstein, N. J. Social influence: Compliance and conformity. Annual Review of Psychology 55, (2004), 591--621.Google ScholarCross Ref
- Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., & Riedl, J. Is seeing believing? How recommender interfaces affect users' opinions. Proc. CHI 2003, ACM Press (2003), 585--592. Google ScholarDigital Library
- Deutsch, M. & Gerard, H. B. A study of normative and informational social influences upon individual judgment. Journal of Abnormal Psychology 51, (1955), 629--636.Google ScholarCross Ref
- Evans, B. & Chi, E. H. Towards a model of understanding social search. Proc. CSCW 2008, ACM Press (2008), 485--494. Google ScholarDigital Library
- Freyne, J., Farzan, R., Brusilovsky, P., Smyth, B., & Coyle, M. Collecting community wisdom: Integrating social search & social navigation. Proc. IUI 2007, (2007), 52--61. Google ScholarDigital Library
- Hanson, W. A. & Putler, D. S. Hits and misses: Herd behavior and online product popularity. Marketing Letters 7, 4 (1996), 297--305.Google ScholarCross Ref
- Hill, W., Stead, L., Rosenstein, M., & Furnas, G. Recommending and evaluating choices in a virtual community of use. Proc. CHI 1995, ACM Press, (1995), 194--201. Google ScholarDigital Library
- Imhoff, R., & Erb, H.-P. What motivates nonconformity? Uniqueness seeking blocks majority influence. Personality and Social Psychology Bulletin 35, (2009), 309--320.Google ScholarCross Ref
- Järvelin, K. & Kekäläinen, J. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20, (2002), 422--446. Google ScholarDigital Library
- Joachims, T., Freitag, D., & Mitchell, T. WebWatcher: A tour guide for the World Wide Web. Proc. ICML (1997), 770--777.Google Scholar
- Joachims, T., Granka, L., Pan, B., Hembrooke, H. & Gay, G. Accurately interpreting clickthrough data as implicit feedback. Proc. SIGIR 2005, ACM Press (2005), 154--161. Google ScholarDigital Library
- Keane, M. T., O'Brien, M. & Smyth, B. Are people biased in their use of search engines? CACM 51, 2 (2008), 49--52. Google ScholarDigital Library
- Kelly, D., Gyllstrom, K., & Bailey, E. W. A comparison of term and query suggestion features for interactive searching. Proc. SIGIR 2009, ACM Press, (2009), 371--378. Google ScholarDigital Library
- Ludford, P. J., Cosley, D., Frankowski, D., & Terveen, L. Think different: Increasing online community participation using uniqueness and group dissimilarity. Proc. CHI 2004, ACM Press (2004), 631--638. Google ScholarDigital Library
- Rook, L. An economic psychological approach to herd behavior. Journal of Economic Issues XL, 1 (2006), 75--95.Google ScholarCross Ref
- Senecal, S. & Nantel, J. The influence of online product recommendations on consumers' online choices. Journal of Retailing 80, (2004), 159--169.Google ScholarCross Ref
- Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., & Boydell, O. Exploiting query repetition and regularity in an adaptive community-based Web search Engine. User Modeling and User-Adapted Interaction 14,5 (2004), 382--423. Google ScholarDigital Library
- Suchanek, F. M., Vojnovic, M., & Gunawardena, D. Social tags: Meaning and suggestions. Proc. CIKM 2008, ACM Press (2008), 223--232. Google ScholarDigital Library
- Surowiecki, J. The wisdom of crowds. New York, NY: Doubleday, 2004. Google ScholarDigital Library
- Svensson, M., Höök, K., Laaksolahti, J., & Waern, A. Social navigation of food recipes. Proc. CHI 2001, ACM Press (2001), 341--348. Google ScholarDigital Library
- Vakkari, P. Changes in search tactics and relevance judgments when preparing a research proposal: A summary of the findings of a longitudinal study. Information Retrieval 4, (3--4) (2004), 295--310. Google ScholarDigital Library
- Voorhees, E. M. Overview of the TREC 2005 Robust Retrieval Track. Proceedings of 14th Annual Text Retrieval Conference, (TREC-14), 2006.Google Scholar
- White, R. W., Bilenko, M., & Cucerzan, S. Studying the use of popular destinations to enhance web search interaction. Proc. SIGIR 2007, ACM Press (2007), 159--166. Google ScholarDigital Library
Index Terms
- Effects of popularity and quality on the usage of query suggestions during information search
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