Personalized Recommendation of New Video Media Based on Deep Neural Network

In order to push videos that users are interested in and improve user experience, this paper proposes a personalized video new media recommendation method based on deep neural network algorithm. The algorithm combines deep learning and content-based recommendation algorithm. By constructing a deep network model, it extracts the features of video and users’ text information, completes the distributed feature representation of video and users at the semantic space level, deeply excavates the potential relationship between users and video, and makes video recommendation. The experimental results show that the AUC index of this algorithm increases first and then decreases, while the RMSE index decreases first and then increases. When the learning rate is around 0.03, the AUC and RMSE reach the maximum and minimum values at the same time. This shows that when the learning rate is 0.03, the performance of the system is the best at this time. The AUC index and map index of the recommendation algorithm based on the deep semantic model proposed in this paper are higher than the other three algorithms, while the RMSE index is lower than them. Conclusion: the results of the recommendation algorithm proposed in this paper are better than the comparison algorithm, and can complete the video recommendation task better.