通过整合DQN和LSTM来设计消费者行为预测和优化模型
Na Liu1, Dajiang Hu1
1School of Business, Chongqing City Management College, Chongqing, China.
本研究介绍了RL-Trans框架,将深度强化学习 (DQN) 和变压器模型结合起来,以智能地捕捉和分析消费者兴趣的变化,以实现个性化的电子商务营销.
科学领域:
- 人工智能的人工智能
- 电子商务分析 电子商务分析
- 消费者行为建模模型
背景情况:
- 电子商务的增长需要先进的方法来了解消费者的利益.
- 现有的方法很难动态地适应不断变化的消费者行为.
- 准确的消费者兴趣识别对于有针对性的营销至关重要.
研究的目的:
- 引入RL-Trans框架用于智能消费者利益分析.
- 在电子商务中增强个性化的营销策略.
- 为消费者行为分析提供一种新的方法.
主要方法:
- 利用具有多头注意力的变压器网络来处理消费者行为数据.
- 集成的深度强化学习 (DQN) 来优化分析模型.
- 开发了一个增强的预测层,用于精细的消费者兴趣分析.
主要成果:
- 与基于LSTM的方法相比,RL-Trans表现出更高的性能.
- 该框架实现了与最先进的方法相比具有竞争力的有效性.
- 实验结果验证了模型适应动态消费者行为的能力.
结论:
- 该RL-Trans框架为消费者利益分析提供了一种新且有效的方法.
- 该方法提供了电子商务的理论基础和实际见解.
- 该研究通过智能数据分析推进了个性化服务和营销策略.
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