A location-aware news feed system enables mobile users to share geo-tagged user-generated messages. In this thesis, we present MobiFeed, a framework designed for scheduling news feeds for mobile users. In MobiFeed, a location prediction function is designed to estimate a mobile user’s locations based on a path prediction algorithm. A relevance measure function is used to determine the relevance of a message to a user. A news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. As a further step, we argue that diversity helps users discover new places and activities. To this end, we extend MobiFeed by enabling a user to specify the minimum number of categories (l) for the messages in a news feed. Our objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least l different categories, and (ii) their total relevance to the user is maximized. Moreover, we study yet another kind of feed service, namely advertising, in temporary social networks (TSNs). Simply broadcasting all the vendors’ advertisements to all users in the TSN may risk the service provider losing its fans. To this end, we present Capacity-Aware Location-Based Advertising (CALBA), which is a framework designed for TSNs to select vendors as advertising sources for mobile users. Our goal is to maximize the overall relevance of selected vendors for a user with the constraint that the total advertising frequency of the selected vendors should not exceed the user’s specified capacity. For each proposed framework above, its performance is evaluated using a real road map and a real social network data set. All experimental results show that our frameworks are efficient and scalable for a large number of messages or advertisements.