Neural network-driven user behavior forecasting and personalized recommendation in power marketing
Liang Yu, Yuanshen Hong, Zhixin Liu, Zheng Wang, Xiangzheng Zhao
TL;DR
This paper introduces a neural network model that improves user behavior prediction and personalized recommendations in power marketing, leading to better customer experiences and business efficiency.
Contribution
A novel neural network model combining GCN, DDPG, and MLP for dynamic and personalized power marketing recommendations.
Findings
The model achieves 5.4% higher precision compared to state-of-the-art methods.
It improves recall by 7.7% and AUC by 3.3%.
The model adapts to user behavior and real-time feedback more effectively.
Abstract
With the development of smart grids and the complexity of power marketing, accurately predicting users behavior and recommending suitable products to users become more and more important in power marketing. However, current methods still have some problems such as data sparsity, cold-start problem, fixed recommendation strategy and hard to adapt users dynamic behavior. It affects the recommendation accuracy of power marketing and bring bad experience to customers. To solve these problems, we propose a new neural network model for user behavior prediction and personalized recommendation in power marketing. Our model uses Graph Convolutional Networks to model user-product interaction relationship, Deep Deterministic Policy Gradient to optimize recommendation strategy in dynamic ways, and Multi-Layer Perceptron to predict user behavior. These three models work together and use their…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Technologies in Various Fields · Advanced Data and IoT Technologies