Grouper: A Dynamic Clustering Interface to Web Search Results

Abstract Users of Web search engines are often forced to sift through the long ordered list of document `snippets' returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on most major search engines. The NorthernLight search engine organizes its output into `custom folders' based on pre-computed document labels, but does not reveal how the folders are generated or how well they correspond to users' interests. In this paper, we introduce Grouper, an interface to the results of the HuskySearch meta-search engine, which dynamically groups the search results into clusters labeled by phrases extracted from the snippets. In addition, we report on the first empirical comparison of user Web search behavior on a standard ranked-list presentation versus a clustered presentation. By analyzing HuskySearch logs, we are able to demonstrate substantial differences in the number of documents followed, and in the amount of time and effort expended by users accessing search results through these two interfaces.

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