Abstract
In order to improve the marketing effect of e-commerce products, based on machine learning algorithms, this paper constructs an e-commerce product marketing model based on machine learning and SVM. Moreover, this paper studies the classic reinforcement learning algorithm Q-learning and proposes an improved Q-learning algorithm. In addition, this paper uses the method of mean standardization to reduce the noise impact of the reward signal caused by the unfixed time interval between decision points, further constructs a standardization factor for the deviation caused by the asynchronous update of the time interval in the iterative process of the Q value function and updates the standardization factor according to the update method of the value function, and proposes the Interval-Q algorithm. At the same time, in view of the fact that traditional reinforcement learning algorithms cannot effectively deal with the observable part of customer status in direct marketing scenarios, based on the deep reinforcement learning DQN model, this paper combines with the idea of hybrid model to propose a DQN model based on dual networks. Finally, this paper uses public data sets for model training and simulation environment construction and then evaluates the algorithm proposed in this paper from different angles and analyses model performance based on examples. From the research results, it can be seen that the precision marketing model constructed in this paper has a good effect and can be applied to practice.
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Acknowledgements
This work was supported by National Project of the Key Research Base for Philosophy and Social Sciences in Shaanxi (ID: 18JZ037), Natural Science Foundation of China (71802158, 71502070), Shaanxi Social Science Fund (2018S42), Special research projects of Shaanxi Provincial Department of Education (739) and Northwestern University National Social Science Fund project incubation project (17XNFH060).
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Cui, F., Hu, H. & Xie, Y. An intelligent optimization method of E-commerce product marketing. Neural Comput & Applic 33, 4097–4110 (2021). https://doi.org/10.1007/s00521-020-05548-5
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DOI: https://doi.org/10.1007/s00521-020-05548-5