## 1 Introduction  
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]. Due to the limitation of network traffic, mobile videos are characterized by their wide variety, single content, and short duration [2]. Until now, many applications (apps), such as Tik Tok, Kuaishou, Watermelon, etc., have become popular mobile video playback platforms and the main entertainment channels that consume people's leisure time in life. For large-scale mobile video resources, how to help users quickly and effectively find mobile videos of interest to further provide personalized mobile video recommendation services has become the development trend of mobile video applications. In real life, people tend to have different understandings and preferences for the same mobile video based on their cultural backgrounds, aesthetic standards, and environments. Therefore, a key issue for personalized video recommendation is to obtain users' interests [3]. The research of personalized recommendation has widely recognized the importance of user preference modeling [4], which aims to storage and manage user preferences for predicting a personalized interest by recording and learning user historical behaviors [5]. Mobile video are ultimately viewed and understood by people. Therefore, how to effectively represent mobile video content by extracting powerful descriptive features is the basic premise of user preference modeling for personalized recommendation. Fortunately, many researchers have made many outstanding works in the field of feature representation of mobile videos [6]. In addition, the mobile video platform generally allows users to add tags to the uploaded mobile videos. Effective tags can provide semantic clues of the video content, as well as reflect the user preferences [7]. As we know, tags and visual features can express the content of mobile videos from two different levels, that is to say, the information of them is often complementary. Therefore, it is a better way for user preference modeling by combining visual features with social tags. In our previous works, 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. A general and widely validated strategy is to represent the summary description of a mobile video with key frames [9]. This is because mobile video key frames can be utilized to illustrate the main content of a mobile video by a single or few frames [10]. In recent years, various deep learning networks have been proposed, such as VGG (Visual Geometry Group) Net [11], GoogLeNet [12], ResNet [13], etc., which have been widely applied in the fields of image classification, image recognition, target detection, and semantic segmentation [14] and have achieved superior performance over traditional methods. This is because deep learning allows computational models that are composed of multiple processing layers to learn data representation with multiple levels of abstraction. Furthermore, deep learning discovers intricate structures in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer [15]. 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.