Abstract
In the information age, the availability of data on consumer profiles has opened new possibilities for companies to increase their revenue via data mining techniques. One approach has been to strategically set prices of various products, taking into account the profiles of consumers. We study algorithms for the multi-product pricing problem, where, given consumer preferences among products, their budgets, and the costs of production, the goal is to set prices of multiple products from a single company, so as to maximize the overall revenue of the company. We present approximation algorithms as well as negative results for several variants of the multi-product pricing problem, modeling different purchasing patterns and market assumptions.
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Aggarwal, G., Feder, T., Motwani, R., Zhu, A. (2004). Algorithms for Multi-product Pricing. In: Díaz, J., Karhumäki, J., Lepistö, A., Sannella, D. (eds) Automata, Languages and Programming. ICALP 2004. Lecture Notes in Computer Science, vol 3142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27836-8_9
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DOI: https://doi.org/10.1007/978-3-540-27836-8_9
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