Analysis and Presentation of Results for Mobile Local Search

Abstract

Aggregation of long lists of concepts is important to avoid overwhelming a small display. Focusing on the domain of mobile local search, this paper presents the development of an application to perform filtering and aggregation of results obtained through the Yahoo! Local web service. First, we performed an analysis of the data available through Yahoo! Local by crawling its database with over 170 thousand local listings located in Chicago. Then, we compiled resources and developed algorithms to filter and aggregate local search results. The methods developed exploit Yahoo!’s listings categorization to reduce the result space and pinpoint the category containing the most relevant results. Finally, we evaluated a prototype through a user study, which pitted our system against Yahoo! Local and against a plain list of search results. The results obtained from the study show that our aggregation methods are quite effective, cutting down the number of entries returned to the user by 43% on average, but leaving search efficiency and user satisfaction unaffected.

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