Latent representation models for user sequential mobility and social influence propagation

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, e.g., Markov chain model. 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. First, we examine user mobility in location-based social networks (LBSNs), where users share their locations by checking-in points-of-interest (POI). Based on the users’ check-in sequences, we consider two research tasks. The first one is next new POI recommendation problem in which new POIs relating to users’ current location are to be recommended. The second one is to predict users who will visit a given POI in a given future period. The complexity of these problems is to precisely learn POI sequential transition, user preference, and the geographical influence. To solve this difficulty, we develop two latent presentation models. One is personalized ranking metric embedding method (PRME), which embeds POIs in a latent Euclidean space. The other is POI2Vec model, which directly incorporates the geographical influence and further jointly considers the user preference. Experimental studies demonstrate the superiority of our proposed approach over the state-of-the-art algorithms in both next new POI recommendation and future visitor prediction. Second, we develop a new latent representation model Inf2vec to learn representations of users in a social network, such that the social influence is captured. As a fundamental problem in social influence propagation analysis, learning influence parameters has been investigated. Most of the existing methods are proposed to estimate i the propagation probability for each edge in social networks. However, they cannot exactly learn the propagation parameters of edges due to data sparsity. Different from the conventional methods, we introduce a social influence embedding problem, which is to learn parameters for nodes rather than edges. We develop a new model Inf2vec, which combines the local social influence context and global user similarity to learn the representations. We conduct extensive experiments on two real datasets, and the results indicate that Inf2vec significantly outperforms the baseline algorithms. Lastly, based on the social influence propagation, we investigate the problem of influence maximization with novelty decay (IMND). Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for this problem. However, they have not considered the impact of novelty decay in influence propagation, i.e., repeated exposures will have diminishing influence on users. We investigate the effect of novelty decay on influence propagation in real-life datasets and formulate the IMND problem. We further analyze its relevant properties and propose an efficient influence estimation technique to solve it. Empirical results on four social networks show the effectiveness and efficiency of our algorithms.