Latent representation models for user sequential mobility and social influence propagation
Date of Issue2017-09-26
Interdisciplinary Graduate School (IGS)
Nanyang Environment and Water Research Institute
With the increasing popularity of online social media applications, a large amount of data has been generated by users. Based on the user generated data, many research problems have been studied, such as the location-based recommendation and social influence analysis. In this thesis, we investigate the problem of user sequential mobility and the problem of social influence propagation. The main challenge of both problems lies in the difficulty to effectively learn the sequential transition. However, due to the data sparsity, it is hard to model the sequential information by conventional methods. To this end, we resort to the latent representation approach, which is to represent items in a low-dimensional latent space, such that the relations between items are captured by their representations. In addition, based on the social influence propagation in social networks, we study the problem of finding a set of influential users.