Browsing by Subject "Mobile search"

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Open Access
Integrating social factors into mobile local search
(2015-08) Kahveci, Basri
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.
Open Access
Integrating social features into mobile local search
(Elsevier Inc., 2016) Kahveci, B.; Altıngövde, İ. S.; Ulusoy, Özgür
As availability of Internet access on mobile devices develops year after year, users have been able to make use of search services while on the go. Location information on these devices has enabled mobile users to use local search services to access various types of location-related information easily. Mobile local search is inherently different from general web search. Namely, it focuses on local businesses and points of interest instead of general web pages, and finds relevant search results by evaluating different ranking features. It also strongly depends on several contextual factors, such as time, weather, location etc. In previous studies, rankings and mobile user context have been investigated with a small set of features. We developed a mobile local search application, Gezinio, and collected a data set of local search queries with novice social features. We also built ranking models to re-rank search results. We reveal that social features can improve performance of the machine-learned ranking models with respect to a baseline that solely ranks the results based on their distance to user. Furthermore, we find out that a feature that is important for ranking results of a certain query category may not be so useful for other categories.