Integrating social factors into mobile local search
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As availability of internet access on mobile devices develops year after year, users have been able to make use of mobile internet and search services while on the go. Location information on these devices has enabled mobile users to utilize local search applications for discovering places and activities around them. Although mobile local search is a kind of search activity, it is inherently di erent than general web search. Mobile local search focuses on local businesses and points of interest, instead of web pages as in general web search. Moreover, users' context has a signi cant e ect on their decision process. In previous studies, ranking signals and user context have been investigated on a small set of features. We extend ranking signals and user context in mobile local search with using data of location-based social networks. We developed a mobile local search application, Gezinio, and collected a data set of local search queries. Gezinio helps users to issue local queries and see various kinds of social information about local businesses around them. We built ranking models and investigated how social features a ect decision process of users. We show that social features in uence users' click decisions and they can be utilized by ranking models to improve the local search experience. Additionally, we propose di erent social features for di erent query categories.