Bao, Jie2014-08-112014-08-112014-06https://hdl.handle.net/11299/164714University of Minnesota Ph.D. dissertation. June 2014. Major: Computer Science. Advisor: Mohamed F. Mokbel. 1 computer file (PDF); ix, 111 pages.With the advances in positioning techniques, such as GPS, cell-towers and WiFi, users can enjoy location-based services more easily than ever before, e.g., on their smart phones and GPS devices. However, with the popularity of recommendation (e.g., Amazon \& Netflix) and socialization (like Facebook and Twitter) functionalities in the web services, users of location-based services are no longer satisfied with the static results returned from a spatial database. At the same time, more and more spatial information, such as geo-tagged photos and check-ins are generated in the traditional social networking services. As a result, users are calling for the next generation of location-based service, i.e., location aware news feed and recommendations, which can provide the user with the more personalized and socialized services.In this thesis, I present my vision of the next generation of location-aware service, which enables the social networking services with location awareness. First of all, in this thesis, I present the unique properties that location information brings to the traditional social networking and recommendation services. After that, I summarize the potential challenges in building efficient and effective location-aware news feed and recommendation services from both the system and user's perspectives. Then, I present a prototype system, (i.e., Sindbad, a location-aware social networking system), to demonstrate three main services provided in the system, as: 1) location-aware news feed service, which efficiently returns the user with spatial-aware messages from her subscribed friends. 2)~location-aware news ranking services, which provide a set of efficient news ranking and message updating algorithms for the user to get more relevant news, based on her social-spatial preferences. And 3)~location-aware recommendations, which provides suggestions based on the social knowledge from the local experts and the preferences mined from a user's location history. Finally, the thesis is concluded with the overall contributions and some potential further research directions.en-USLocation-based servicesRecommendationsSocial networkingThesis or Dissertation