Multi-modal learning for video recommendation based on mobile application usage

The increasing popularity of mobile devices has brought severe challenges to device usability and big data analysis. In this paper we investigate the intellectual recommender system on cell phones by incorporating mobile data analysis. Nowadays with the development of smart phones, more and more applications have emerged on various areas, such as entertainment, education and health care. While these applications have brought great convenience to people's daily life, they also provide tremendous opportunities for analyzing users' interests. In this work we develop an Android background service to collect the user behaviors and analyze their preferences based on their Android application usage. As one of the most intuitive media for visual representation, videos with various types of contents are recommended to users based on a proposed graphical model. The proposed model jointly utilizes the textual descriptions of Android applications and videos, as well as the extracted video content based features. Besides, by analyzing the user's habit of application usage we seamlessly integrate the user's personal interests during the recommendation. The extensive comparisons to multiple baselines reveal the superiority of the proposed model on the recommendation quality. Furthermore, we conduct experiments on personalized recommendation to demonstrate the capacity of the proposed model in effectively analyzing the user's personal interests.