DocumentCode :
3717212
Title :
Multi-modal learning for video recommendation based on mobile application usage
Author :
Xiaowei Jia;Aosen Wang;Xiaoyi Li;Guangxu Xun;Wenyao Xu;Aidong Zhang
Author_Institution :
School of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA, 14260-1660
fYear :
2015
Firstpage :
837
Lastpage :
842
Abstract :
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.
Keywords :
"Smart phones","Yttrium","Training","Graphical models","Mobile applications","Mobile communication"
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BigData.2015.7363830
Filename :
7363830
Link To Document :
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