TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

• We propose a probabilistic model by generalizing LDA to capture the behaviors of users in Twitter.
• A variational EM algorithm is presented for our model.
• We develop recommendation algorithms using the parameters estimated by the inference algorithm.
• We did performance study to show the effectiveness of our model and recommendation algorithms.
• Our recommendation algorithms are much more accurate than traditional algorithms in experiments.

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.