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.