Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning
With the rapid development of the data trading market, traditional fixed pricing strategies can no longer effectively reflect the real value of data products, thereby restricting the development of the data trading market. To address this challenge, this paper proposes a dynamic pricing method for d...
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| Format: | Article |
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10810405/ |
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| author | Junxin Shen Yashi Wang Fanghao Xiao |
| author_facet | Junxin Shen Yashi Wang Fanghao Xiao |
| author_sort | Junxin Shen |
| collection | DOAJ |
| description | With the rapid development of the data trading market, traditional fixed pricing strategies can no longer effectively reflect the real value of data products, thereby restricting the development of the data trading market. To address this challenge, this paper proposes a dynamic pricing method for data products based on deep reinforcement learning, aiming to attract buyers through dynamic pricing strategies and maximize the cumulative profits of data sellers, thus driving the further development of the data trading market. First, the dynamic pricing problem for data products is modeled as a Markov Decision Process (MDP). Then, a dynamic pricing algorithm based on the Deep Q-learning algorithm is designed, incorporating an annealing mechanism to optimize the exploration strategy. Finally, the performance of traditional reinforcement learning algorithms, specifically Q-learning and SARSA, is compared. The experimental results show that the dynamic pricing method based on deep reinforcement learning not only effectively enhances the cumulative profits of sellers but also significantly improves both revenue and algorithm performance compared to static pricing strategies and traditional reinforcement learning algorithms. This research provides a novel solution for data product pricing, contributing to the healthy development of the digital economy. |
| format | Article |
| id | doaj-art-a8cff29ce4b34fd4ba2bbf796a083bac |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a8cff29ce4b34fd4ba2bbf796a083bac2024-12-28T00:01:12ZengIEEEIEEE Access2169-35362024-01-011219482919483810.1109/ACCESS.2024.352067010810405Dynamic Pricing Strategy for Data Product Through Deep Reinforcement LearningJunxin Shen0https://orcid.org/0000-0001-7671-6636Yashi Wang1Fanghao Xiao2https://orcid.org/0000-0003-2390-8399School of Economics and Management, Kunming University of Science and Technology, Kunming, ChinaSchool of Economics and Management, Kunming University of Science and Technology, Kunming, ChinaMarxist College, Xiamen Institute of Technology, Xiamen, ChinaWith the rapid development of the data trading market, traditional fixed pricing strategies can no longer effectively reflect the real value of data products, thereby restricting the development of the data trading market. To address this challenge, this paper proposes a dynamic pricing method for data products based on deep reinforcement learning, aiming to attract buyers through dynamic pricing strategies and maximize the cumulative profits of data sellers, thus driving the further development of the data trading market. First, the dynamic pricing problem for data products is modeled as a Markov Decision Process (MDP). Then, a dynamic pricing algorithm based on the Deep Q-learning algorithm is designed, incorporating an annealing mechanism to optimize the exploration strategy. Finally, the performance of traditional reinforcement learning algorithms, specifically Q-learning and SARSA, is compared. The experimental results show that the dynamic pricing method based on deep reinforcement learning not only effectively enhances the cumulative profits of sellers but also significantly improves both revenue and algorithm performance compared to static pricing strategies and traditional reinforcement learning algorithms. This research provides a novel solution for data product pricing, contributing to the healthy development of the digital economy.https://ieeexplore.ieee.org/document/10810405/Data trading marketdynamic pricingdeep reinforcement learningdigital economy |
| spellingShingle | Junxin Shen Yashi Wang Fanghao Xiao Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning IEEE Access Data trading market dynamic pricing deep reinforcement learning digital economy |
| title | Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning |
| title_full | Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning |
| title_fullStr | Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning |
| title_full_unstemmed | Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning |
| title_short | Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning |
| title_sort | dynamic pricing strategy for data product through deep reinforcement learning |
| topic | Data trading market dynamic pricing deep reinforcement learning digital economy |
| url | https://ieeexplore.ieee.org/document/10810405/ |
| work_keys_str_mv | AT junxinshen dynamicpricingstrategyfordataproductthroughdeepreinforcementlearning AT yashiwang dynamicpricingstrategyfordataproductthroughdeepreinforcementlearning AT fanghaoxiao dynamicpricingstrategyfordataproductthroughdeepreinforcementlearning |