Twitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we first propose a spatio-temporal topic model, i.e., Upstream Spatio-Temporal Topic Model (USTTM). USTTM can discover temporal topics and regions, i.e., a user's choice of region and topic is affected by time in this model. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In addition, USTTM captures the property that user's interests and activity space will change overtime, and users have different region and topic distributions at different times in USTTM. However, both USTTM and other related models capture “microscopic patterns” within a single city, where users share POIs, and cannot discover “macroscopic” patterns in a global area, where users check-in to different POIs. Therefore, we also propose a macroscopic spatio-temporal topic model, MSTTM, employing words of tweets that are shared between cities to learn the topics of user interests. We perform an experimental evaluation on Twitter and Gowalla data sets from New York City and on a Twitter US data set. In our qualitative analysis, we perform experiments with USTTM to discover temporal topics, e.g., how topic “tourist destinations” changes over time, and to demonstrate that MSTTM indeed discovers macroscopic, generic topics. In our quantitative analysis, we evaluate the effectiveness of USTTM in terms of perplexity, accuracy of POI recommendation, and accuracy of user and time prediction. Our results show that the proposed USTTM achieves better performance than the state-of-the-art models, confirming that it is more natural to model time as an upstream variable affecting the other variables. Finally, the performance of the macroscopic model MSTTM is evaluated on a Twitter US dataset, demonstrating a substantial improvement of POI recommendation accuracy compared to the microscopic models.

Twitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we propose two spatio-temporal topic models: Downstream Spatio-Temporal Topic Model (DSTTM) and Upstream Spatio-Temporal Topic Model (USTTM). Both models can discover temporal topics and regions. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In order to capture the property that user's interests and activity space will change over time, we propose the USTTM, where users have different region and topic distributions at different times. We conduct experiments on Twitter and Gowalla data sets. In our quantitative analysis, we evaluate the effectiveness of our models by the perplexity, the accuracy of POI recommendations, and user prediction, demonstrating that our models achieve better performance than the state-of-the-art models.

In this paper, we address the problem of recommending Point-of-Interests (POIs) to users in a location-based social network. To the best of our knowledge, we are the first to propose the ST (Social Topic) model capturing both the social and topic aspects of user check-ins. We conduct experiments on real life data sets from Foursquare and Yelp. We evaluate the effectiveness of ST by evaluating the accuracy of top-k POI recommendation. The experimental results show that ST achieves better performance than the state-of-the-art models in the areas of social network-based recommender systems, and exploits the power of the location-based social network that has never been utilized before.

Mobile networks enable users to post on social media services (e.g., Twitter) from anywhere and anytime. This new phenomenon led to the emergence of a new line of work of mining the behavior of mobile users taking into account the spatio-temporal aspects of their engagement with online social media. In this paper, we address the problem of recommending the right locations to users at the right time. We claim to propose the first comprehensive model, called STT (Spatio-Temporal Topic), to capture the spatio-temporal aspects of user check-ins in a single probabilistic model for location recommendation. Our proposed generative model does not only captures spatio-temporal aspects of check-ins, but also profiles users. We conduct experiments on real life data sets from Twitter, Go Walla, and Bright kite. We evaluate the effectiveness of STT by evaluating the accuracy of location recommendation. The experimental results show that STT achieves better performance than the state-of-the-art models in the areas of recommender systems as well as topic modeling.

Mobile networks enable users to post on social media services (e.g., Twitter) from anywhere. The activities of mobile users involve three major entities: user, post, and location. The interaction of these entities is the key to answer questions such as who will post a message where and on what topic? In this paper, we address the problem of profiling mobile users by modeling their activities, i.e., we explore topic modeling considering the spatial and textual aspects of user posts, and predict future user locations. We propose the first ST (Spatial Topic) model to capture the correlation between users' movements and between user interests and the function of locations. We employ the sparse coding technique which greatly speeds up the learning process. We perform experiments on two real life data sets from Twitter and Yelp. Through comprehensive experiments, we demonstrate that our proposed model consistently improves the average [email protected],5,10,15,20 for location recommendation by at least 50% (Twitter) and 300% (Yelp) against existing state-of-the-art recommendation algorithms and geographical topic models.

As social networking is moving into the web, the study and exploitation of social correlation has emerged as a hot research topic. Most of these work consider binary social relations, called "friendships". However, online users tend to establish many friendships of varying degree of strength, e.g., relatives, friends, co-workers, and acquaintances. We argue that, due to their different degree of strength, different friend relationships will have greatly varying degrees of correlation and should be distinguished. Besides, social correlation is not the only factor driving user behavior. In this paper, we address the problem of learning the strength of the social correlation, user, item, and sparsity factors in online social networks. We propose a probabilistic model, Factor Weight Model, for learning these strengths which maximize the joint probability of the observed user behavior, i.e., actions on items. Different from existing methods, our model considers not only social correlation, but it also considers the other factors affecting user behavior. We have conducted experiments on four real life data sets from Epinions, Flixster, Flickr, and Digg. Our experiments prove the superiority of our model over a state-of-the-art method in terms of action prediction. We also analyze the contributions of the various factors for the prediction performance.

In recent years, social media websites, such as Epinions, Twitter, and Google+, have gained in popularity and have become ubiquitous in our daily lives, where rich user-generated texts are propagated through social networks. Topic models, such as Latent Dirichlet Allocation (LDA), have been proposed and shown to be useful for text analysis. The existing topic models focus on traditional document collections, which consist of a relatively small number of long and high-quality documents. However, user-generated texts tend to be shorter and noisier than traditional content. Besides, the social networks have two novel features: context information on nodes, such as user features, and edges, such as relationship, which have not been considered by the existing topic models. In this paper, we pose the problem of finding user topics in large-scale collection of documents from online social networks. We propose a comprehensive Feature based and a Social based Topic model, taking into account the user features and social networks. We demonstrate that our models have better performance than a baseline LDA in the Epinions, Twitter, and Google+ data sets.