- (Li et al., 2012) ⇒ Rui Li, Shengjie Wang, Hongbo Deng, Rui Wang, and Kevin Chen-Chuan Chang. (2012). “Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339692
Users' locations are important to many applications such as targeted advertisement and news recommendation. In this paper, we focus on the problem of profiling users' home locations in the context of social network (Twitter). The problem is nontrivial, because signals, which may help to identify a user's location, are scarce and noisy. We propose a unified discriminative influence model, named as UDI, to solve the problem. To overcome the challenge of scarce signals, UDI integrates signals observed from both social network (friends) and user-centric data (tweets) in a unified probabilistic framework. To overcome the challenge of noisy signals, UDI captures how likely a user connects to a signal with respect to 1) the distance between the user and the signal, and 2) the influence scope of the signal. Based on the model, we develop local and global location prediction methods. The experiments on a large scale data set show that our methods improve the state-of-the-art methods by 13%, and achieve the best performance.
|2012 TowardsSocialUserProfilingUnifi||Rui Li|
Kevin Chen-Chuan Chang
|Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations||10.1145/2339530.2339692||2012|
|Author||Rui Li +, Shengjie Wang +, Hongbo Deng +, Rui Wang + and Kevin Chen-Chuan Chang +|
|proceedings||Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations +|