WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that an innovative personalized multi-modal video recommendation system is developed. It employs deep learning method and multi-modal data analysis. The system utilizes deep learning algorithms to mine hidden features of movies and users, and is trained with multi-modal data to further predict video ratings to provide more accurate personalized recommendation results.
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This recommendation system, uses deep learning and a holistic process model for multi-modal data. First, we collect datasets containing multi-modal information about users and videos. Then, we transform the parameters of users and videos into single-valued matrices containing non-zero singular values. Next, we train a convolutional neural network (CNN) with multi-layer convolutional filters to improve the level classification of the data. By training the model, we use the refined features to find potential relationships between users and movies and make recommendations based on similarity criteria. Finally, we recommend videos for users based on similarity theory.
The video recommendation system includes, data collection and pre-processing, feature extraction and representation learning, model training and optimization, and recommendation algorithm and personalized recommendation.
Data collection and pre-processing: By containing multi-modal datasets of users and videos, including information such as textual descriptions, images and audio. These data can be obtained from video databases, user behaviors and other available resources. In the data pre-processing phase, the data is cleansed, denoised and normalized to ensure data consistency and usability.
Feature extraction and representation learning: To mine hidden features of users, a deep learning method is used for feature extraction and representation learning. Through natural language processing such as word embedding and recurrent neural networks (RNNs) to transform texts into distributed vector representations. For image and audio data, use CNN and RNN for feature extraction.
Model training and optimization: Construct deep learning network models and train and optimize them using training data. During model training, the weights and biases of the model are updated by the back propagation algorithm and gradient descent optimizer to minimize the prediction error. At the same time, e.g., regularization and batch normalization are used to improve the generalization ability of the model and prevent overfitting.
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Recommendation algorithm and personalized recommendations: Video recommendations can be made using features and patterns learned by a trained model. Personalized recommendations are made by calculating the similarity between the user and the video based on the user’s historical behavior and preferences. Based on the similarity calculation, a list of video recommendations is generated for the user and optimized based on user feedback and ratings.
WiMi’s personalized video recommendation system has better recommendation accuracy and user satisfaction compared to traditional recommendation algorithm such as collaborative filtering, content-based filtering and singular value decomposition. At the same time, the system can also alleviate the data sparsity problem to a certain extent and improve the diversity of recommendations.
For future development, WiMi’s researchers have made some suggestions for improvement. First, data quality and diversity should be further improved to ensure the accuracy and coverage of the recommendation system. Second, improving the interpretation ability for the recommendation models is also an important direction to enable users to understand the basis of recommendation results and increase the transparency and trust of the system. In addition, with the popularity of mobile devices and the growth of online video services, real-time and online recommendations are becoming increasingly important. Future research could explore how to perform efficient personalized recommendations in real-time environments, combining recommendation models and real-time data stream processing to achieve instant recommendation responses.
WiMi’s personalized video recommendation system shows great potential in solving the information overload problem. It not only provides more accurate and personalized recommendation results, but also alleviates the data cold-start sparsity problem and improves user experience. Future research and development will further improve the recommendation algorithm to make the recommendation system more intelligent and reliable, and bring a better viewing experience to users.
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