Deep Reinforcement Learning-Based Recommender Algorithm Optimization and Intelligent Systems Construction for Business Data Analysis
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Öz
Business analysis plays an essential role in numerous companies and industries, which involves data collection, data analysis, and the construction of intelligent systems to make critical business decisions. By using a data model to filter through consumers' data, it is easier than ever to make recommendations to consumers for what they would enjoy using or purchasing. Added a real-time product Recommendation System framework, the Reinforcement Learning model is used to automatically learn the optimal recommendation strategy. Also, the Deep Q-Network (DQN) algorithm is modified by replacing the convolutional neural network with the Long Short-Term Memory (LSTM). The solution analyzes and predicts consumers' preferences in real-time, and makes up for the lack of iterative optimization of traditional Recommendation Systems in real scenarios.
Publication details
- DOI
- 10.1109/ipec54454.2022.9777623
- OpenAlex
- W4281392340
- Document type
- conference-paper
- Language
- EN
- Source
- 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)
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