Susan Dumais
Edward Cutrell
Hao Chen
ABSTRACT
We developed and evaluated seven interfaces for integrating semantic category information with Web search results. List interfaces were based on the familiar ranked-listing of search results, sometimes augmented with a category name for each result. Category interfaces also showed page titles and/or category names, but re-organized the search results so that items in the same category were grouped together visually. Our user studies show that all Category interfaces were more effective than List interfaces even when lists were augmented with category names for each result. The best category performance was obtained when both category names and individual page titles were presented. Either alone is better than a list presentation, but both together provide the most effective means for allowing users to quickly examining search results. These results provide a better understanding of the perceptual and cognitive factors underlying the advantage of category groupings and provide some practical guidance to Web search interface designers.
- 1.Chakrabarti, S., Dom, B., Agrawal, R., and Raghavan, P. Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. The VLDB Journal 7, 1998, 163-178. Google Scholar
Digital Library
- 2.Chen, H. and Dumais, S. T. Bringing order to the Web: Automatically categorizing search results. In Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2000, 145-152. Google ScholarDigital Library
- 3.Dumais, S. T. and Chen, H. Hierarchical classification of web content. In Proceedings of SIGIR'2000, 256-263. Google ScholarDigital Library
- 4.Dumais, S. T. and Landauer, T. K. Describing categories of objects for menu retrieval systems. Behavioral Research Methods, Instruments and Computers, 1984, 16(2), 242-248.Google ScholarCross Ref
- 5.Dumais, S. T., Platt, J., Heckerman, D. and Sahami, M. Inductive learning algorithms and representations for text categorization. In Proceedings of ACM-CIKM98, Nov. 1998, 148-155. Google ScholarDigital Library
- 6.Egan, D. E., Lesk, M. E., Ketchum, R. D., Lochbaum, C. C., Remde, J. R., Littman, M. and Landauer, T. K. Hypertext for the electronic library? CORE sample results. In Proceedings of Hypertext'91, 299-312. Google ScholarDigital Library
- 7.Furnas, G. W. Generalized fisheye views. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI),1986, 16-23. Google ScholarDigital Library
- 8.Green, S., Marchionini, G., Plaisant, C., and Shneiderman, B., Previews and overviews in digital libraries: Designing surrogates to support visual information-seeking, Journal of the American Society for Information Science 51(3), March 2000, 380-393. Google ScholarDigital Library
- 9.Mackinlay, J. D., Robertson, G. G., and Card, S. K. The perspective wall: Detail plus context smoothly integrated. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 1991, 173- 179. Google ScholarDigital Library
- 10.Nygren, E. "Between the clicks": Skilled users scanning of pages. In Proceedings of Designing for the Web: Empirical studies, October 1996.Google Scholar
- 11.Pratt, W., Hearst, M. and Fagan, L. A knowledge-based approach to organizing retrieved documents. In Proceedings of AAAI/IAAI, 1999, 80-85. Google ScholarDigital Library
- 12.Stata, R, Bharat, K. and Maghhout, F. The term vector database: Fast access to indexing terms for web pages. In Proceedings of WWW9, 2000, 247-256. Google ScholarDigital Library
- 13.http://search.dmoz.org/Google Scholar
- 14.http://www.looksmart.com/Google Scholar
- 15.http://www.northernlight.com/Google Scholar
- 16.http://www.snap.com/Google Scholar
- 17.http://www.yahoo.com/Google Scholar
Index Terms
- Optimizing search by showing results in context
Recommendations
Bringing order to the Web: automatically categorizing search results
CHI '00: Proceedings of the SIGCHI conference on Human Factors in Computing SystemsWe developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-the-fly. A user study ...
Displaying relevance scores for search results
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalInternet search engines typically compute a relevance score for webpages given the query terms, and then rank the pages by decreasing relevance scores. The popular search engines do not, however, present the relevance scores that were computed during ...
Interest-based personalized search
Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps ...
Comments