발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.4] A Research on the Development of a Product Recommendation Model based on Multi-Agent Reinforcement Learning |
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| 논문투고자 |
Chul-Jin Kim |
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| 논문내용 |
This research aims to develop a product recommendation model using multi-agent reinforcement learning. It was confirmed that the existing product recommendation system did not sufficiently reflect individual tastes and interactions, and had difficulty in handling the complexity of interactions. In this paper, we introduce multi-agent reinforcement learning to develop a model that handles complex interactions and provides effective recommendations. In this study, the development is carried out using the Online Retail dataset provided by UCI, and the architecture and algorithm of the product recommendation model based on multi-agent reinforcement learning are developed. The product recommendation model develops a recommendation model by considering the purchase history of the customer. As a result of the experiment, the product recommendation model based on multi-agent reinforcement learning showed improved recommendation accuracy compared to existing recommendation methods. This study suggests practical applicability to the development and application of a product recommendation model using multi-agent reinforcement learning. Important results that can contribute to the practical application of the product recommendation system were derived, and future research directions and considerations for the actual service application of the model were reviewed. These results provide the basis for the product recommendation field using multi-agent reinforcement learning, and can be expected to be used in the industry. |
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