Browsing by Subject "Location-based social networks"

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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.
Open Access
Location recommendations for new businesses using check-in data
(IEEE, 2016-12) Eravci, Bahaeddin; Bulut, Neslihan; Etemoğlu, C.; Ferhatosmanoğlu, Hakan
Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.