## 8 Conclusions and Future Work  
A personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags in this paper. Firstly, in order to compactly and accurately represent the content of mobile videos, the deep features are extracted by ELU-3 DCNN. Then, user preference is modeled by maximum likelihood estimation and exponential moving average method to have a trade-off between user preference for deep features and user preference for social tags. Finally, a personalized mobile video recommendation system is built based on user preference modeling after detecting key frames with differential evolution optimization algorithm. The experiments are performed in precision and recall, proving the effectiveness of our method. Experimental results have shown our method can significantly improve the precision and recall of recommendation results.  
In further works, the following aspects will be considered: a) a more suitable deep neural network will be used to extract deep features; b) the natural language processing algorithm can be utilized to solve the problems of understanding social tags; c) more information such as rating matrix and user comments will be considered to generate a more accurate user preference modeling. Through designing a better user preference modeling to represent the user interest, a more accurate recommendation system will be built in future works.  
[PERSON] and [PERSON] proposed the research direction and gave the conceptualization. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments, analyzed and interpreted the data. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] supervised the study and reviewed this paper. All authors read and approved the final manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Video Topic [22]**} & \multicolumn{4}{c}{**Tag Ranking [24]**} & \multicolumn{2}{c}{**Our Method**} \\ \hline
**Number of Video** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline k = 1 & 0.75 & 0.03 & 1 & 0.05 & 1 & 0.05 \\ k = 5 & 0.70 & 0.22 & 0.89 & 0.27 & 0.91 & 0.28 \\ k = 10 & 0.61 & 0.34 & 0.86 & 0.48 & 0.93 & 0.53 \\ k = 15 & 0.57 & 0.46 & 0.75 & 0.61 & 0.79 & 0.65 \\ k = 20 & 0.51 & 0.55 & 0.59 & 0.64 & 0.65 & 0.70 \\ k = 25 & 0.45 & 0.60 & 0.50 & 0.68 & 0.54 & 0.72 \\ k = 30 & 0.41 & 0.66 & 0.43 & 0.70 & 0.46 & 0.74 \\ k = 35 & 0.39 & 0.72 & 0.39 & 0.72 & 0.41 & 0.76 \\ k = 40 & 0.36 & 0.76 & 0.36 & 0.76 & 0.37 & 0.79 \\ k = 45 & 0.33 & 0.79 & 0.33 & 0.80 & 0.33 & 0.80 \\ k = 50 & 0.31 & 0.80 & 0.31 & 0.80 & 0.31 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The precision and recall of three methods.  
Figure 13: The precision and recall curve of three methods.  
**Funding:** This work in this paper was supported by the Beijing Municipal Natural Science Foundation Cooperation Beijing Education Committee (No. KZ 201910005007, No. KZ 201810005002) and the National Natural Science Foundation of China (No. 61971016, No. 61531006, No.61602018, and No.61701011).  
**Conflicts of Interest:** The authors declare no conflicts of interest.