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Due to its brief form and growing popularity, microblogging is becoming people’s favorite choice for seeking information and expressing opinions. Messages received by a user mainly depend on whom the user follows. Thus, recommending users with similar interests may improve the experience quality for information receiving. Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users’ preferences by analyzing the user-generated contents. Moreover, users’ interests are not static, on the contrary, they change as time goes by. Based on such intuitions, in this paper, we propose a temporal-topic model to analyze users’ possible behaviors and predict their potential friends in microblogging. The model learns users’ latent preferences by extracting keywords on aggregated messages over a period of time via a topic model, and then the impact of time is considered to deal with interest drifts. The experimental results of friend recommendations on Sina Weibo, one of the most popular microblogging sites in China, have demonstrated the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) project aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to accurately locate the positions of moving vehicles in real time. In this paper, we present an innovative scheme, Hierarchical Exponential Region Organization (HERO), to tackle this problem. In SG, the location information of individual vehicles is actively logged in local nodes which are distributed throughout the city. For each vehicle, HERO dynamically maintains an advantageous hierarchy on the overlay network of local nodes to conservatively update the location information only in nearby nodes. By bounding the maximum number of hops the query is routed, HERO guarantees to meet the real-time constraint associated with each vehicle. A small-scale prototype system implementation and extensive simulations based on the real road network and trace data of vehicle movements from Shanghai demonstrate the efficacy of HERO. [ABSTRACT FROM AUTHOR]