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

Abstract

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.