Abstract: The popularity and development of mobile devices and mobile apps have dramatically changed human life. Due to the tremendous and still rapidly growing number of mobile apps, helping users find apps that satisfy their demands remains a difficult task. To address this problem, we propose a personalized mobile app recommender system based on the textual data of user social Media i.e., public accessible tweets, which can also reflect user’s interest and make up for the sparsity of app usage data. Topic modelling techniques are applied to extract topics of user social media data, and the probability distributions of the topics are utilized to represent the features of the apps. Then, the user profile is constructed based on the user's interest to capture user preferences. Both the topic distributions of the apps anduser preferences are taken into account to produce recommendation scores to generate recommendation lists for target users. We crawl real-world data sets from Twitter to evaluate the performance. The experimental results show that user social data i.e., tweet is effective for deriving the user interest, and the proposed app recommender system improves the performance of existing approaches. Keywords - App recommendation, social media, transfer learning, collaborative filtering