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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >The Recommending Agricultural Product Sales Promotion Mode in E-Commerce Using Reinforcement Learning with Contextual Multiarmed Bandit Algorithms
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The Recommending Agricultural Product Sales Promotion Mode in E-Commerce Using Reinforcement Learning with Contextual Multiarmed Bandit Algorithms

机译：The Recommending Agricultural Product Sales Promotion Mode in E-Commerce Using Reinforcement Learning with Contextual Multiarmed Bandit Algorithms

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摘要

In recent years, sales of agricultural products in Taiwan have been transformed into electronic marketing, and agricultural products with better consumer orientation have been recommended, and farmers' income has been improved through sales websites. In the past, A/B testing was used to determine the degree of preference for website solutions, which required a large number of tests for evaluation, and could not respond to environmental variables that made it difficult to predict the actual recommendation in advance. Therefore, in this study, the reinforcement learning model combined with different contextual Multiarmed Bandit algorithms can be tested in data sets of different complexity, which can actually perform well in changing products. It is helpful to predict the preferences of the promotion model.
机译：近年，台湾农产品销售转型为电子营销，推荐较好消费导向的农产品，透过销售网站提高农民收入。过去，A/B测试用于确定对网站解决方案的偏好程度，这需要大量的测试进行评估，并且无法响应环境变量，这使得难以提前预测实际推荐。因此，在这项研究中，可以将强化学习模型与不同的上下文多臂强盗算法相结合，在不同复杂度的数据集中进行测试，这在变化的产品中实际上可以表现良好。预测促销模型的偏好很有帮助。

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