Sosyal faktörlerin mobil yerel aramalara entegrasyonu

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 different 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 significant effect 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 affect decision process of users. We show that social features influence users' click decisions and they can be utilized by ranking models to improve the local search experience. Additionally, we propose different social features for different query categories.