Abstract
In this paper, we propose an approach of dynamic pricing where buyers purchase decision is dependent on multiple preferred purchase attributes such as product price, product quality, after sales service, delivery time, sellers’ reputation. The approach requires the sellers, by considering the five attributes, to set an initial price of the product with the help of their prior knowledge about prices of the product offered by other competing sellers. Our approach adjusts the selling price of products automatically with the help of neural network in order to maximize seller revenue. The experimental results portray the effect of considering the five attributes in earning revenue by the sellers. Before concluding with directions for future works, we discuss the value of our approach in contrast with related work.
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Ghose, T.K., Tran, T.T. (2010). A Dynamic Pricing Approach in E-Commerce Based on Multiple Purchase Attributes. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_13
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DOI: https://doi.org/10.1007/978-3-642-13059-5_13
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