WiMi Developed a Deep Learning-Based Approach to Personalized Video Recommendations

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provide announced that it developed a personalized video recommendation system based on deep learning according to the development needs of the industry, providing new ideas and directions for the research of personalized video recommendation under deep learning.

Recommended:  Five Things You Should Do to Ace your Customer Service Strategy with Artificial Intelligence

The underlying technical logic of WiMi’s deep learning-based personalized video recommendation system mainly includes the construction of neural network models, feature representation learning, model training and optimization, fusion of contextual information, real-time recommendation and online learning, and the interpretation and interpretability of recommendation results. The application of these technologies can improve the accuracy, degree of personalization, and user experience of the recommendation algorithm and provide users with better video recommendation services:

Neural network models: At the heart of deep learning are neural network models. In personalized video recommendation, different types of neural network models are used to model the association between the user and the video. Neural network models include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short Term Memory Networks (LSTM). These models are able to perform nonlinear transformations and feature extraction through multiple layers of neuronal units to better capture the hidden associations between users and video content.

Feature Representation Learning: In a personalized video recommendation system, effective feature representations are critical to the performance of the model. While traditional recommendation algorithms require features to be more programmatic and modular, deep learning-based approaches can automatically learn feature representations. By introducing structures such as the embedding layer or convolutional layer in neural networks, user and video features can be transformed into low-dimensional dense vectors to better capture their interactions.

Model training and optimization: Deep learning models are usually trained using optimization algorithms such as gradient descent to minimize prediction errors. In personalized video recommendation, optimization algorithms such as stochastic gradient descent (SGD) or Adam are used to update model parameters. To improve the generalization ability of the model and prevent over-fitting, regularization techniques are used. Meanwhile, methods such as batch training or mini-batch training are used to accelerate the training process of the model.

Fusion of contextual information: In personalized video recommendation, the user’s interest and preference may be influenced by contextual information, such as time, location, device, etc. To make recommendations more accurate, contextual information is incorporated into deep learning models. An attention mechanism is used to dynamically adjust the weights between user and video features to reflect the current contextual information.

Real-time recommendation and online learning: Personalized video recommendation needs to respond to user requests in real-time and make recommendations based on real-time behavioral data. Through online learning methods, the model is constantly updated and optimized to adapt to the real-time changes of users. Online learning is achieved through techniques such as incremental training or incremental updating incremental updating, so that the model can obtain the latest user behavioral data in time and make real-time adjustments and optimizations to the model.

Recommended: AiThority Interview with Rebecca Clyde, Co-founder and CEO of Botco.ai

Recommendation result interpretation and interpretability: In personalized video recommendation, the user’s interpretation and interpretability of the recommendation result are very important. In order to increase the interpretability of the recommendation results, techniques such as the attention mechanism and the inference mechanism to explain the generative model are used so as to explain the basis and reasons for the recommendation results to the user. It improves the user’s understanding and acceptance of the recommendation results and enhances the user’s trust and satisfaction.

A practical application of WiMi’s deep learning-based personalized video recommendation system. The core of the system is the recommendation module, which uses deep learning models to model user interests and generate personalized video recommendation results. In practical applications, other techniques and algorithms, such as content-based recommendation and social network analysis, can be combined to further improve the accuracy and diversity of personalized video recommendations. In addition, user feedback can be used to continuously optimize and update the recommendation model to meet the changing interests and needs of users.

WiMi’s deep learning-based personalized video recommendation technology solves information overload, personalizes user needs, improves user experience, and promotes market development in the online video industry. With the continuous progress of artificial intelligence and deep learning technology, personalized video recommendation technology can also be combined with other emerging technologies to develop more application directions. For example, combined with augmented learning technology, the recommendation system can further optimize the recommendation strategy through interactive learning with users; combined with virtual reality and augmented reality technology, the recommendation system can provide a more immersive video viewing experience. Personalized video recommendation technology can be combined with social media and user participation to provide a richer user experience. By analyzing users’ social network information and interactive behaviors, the recommendation system can recommend videos related to their interests and promote communication and sharing among users. This model of social interaction and user participation can increase user stickiness and loyalty, and drive users to generate more content and spread word-of-mouth.

Recommended: Why Managers Should Train More with AI Devices and Intelligent Virtual Assistants

[To share your insights with us, please write to sghosh@martechseries.com]