Abstract
With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.
Highlights
With the rapid development of the mobile Internet and multimedia technology, more and more users browse and watch videos through mobile terminals such as mobile phones and tablets.These videos in mobile terminals are called mobile videos, which are shared, transmitted, and accessed by the mobile network [1]
We proposed a personalized recommendation of social image methods, and the experimental results showed the effectiveness of constructing a user interest model with deep features and social tag trees [8]
A personalized mobile video recommendation system is built based on user preference modeling
Summary
With the rapid development of the mobile Internet and multimedia technology, more and more users browse and watch videos through mobile terminals such as mobile phones and tablets. We proposed a personalized recommendation of social image methods, and the experimental results showed the effectiveness of constructing a user interest model with deep features and social tag trees [8]. Another key issue in personalized mobile video recommendation is to design an effective recommendation mechanism to help users preview the summary of video content and further make a decision. Considering the advantages of deep learning, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags in this paper.
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