Abstract
We study the optimal pricing for revenue maximization over social networks in the presence of positive network externalities. In our model, the value of a digital good for a buyer is a function of the set of buyers who have already bought the item. In this setting, a decision to buy an item depends on its price and also on the set of other buyers that have already owned that item. The revenue maximization problem in the context of social networks has been studied by Hartline, Mirrokni, and Sundararajan [4], following the previous line of research on optimal viral marketing over social networks [5,6,7].
We consider the Bayesian setting in which there are some prior knowledge of the probability distribution on the valuations of buyers. In particular, we study two iterative pricing models in which a seller iteratively posts a new price for a digital good (visible to all buyers). In one model, re-pricing of the items are only allowed at a limited rate. For this case, we give a FPTAS for the optimal pricing strategy in the general case. In the second model, we allow very frequent re-pricing of the items. We show that the revenue maximization problem in this case is inapproximable even for simple deterministic valuation functions. In the light of this hardness result, we present constant and logarithmic approximation algorithms when the individual distributions are identical.
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References
AhmadiPourAnari, N., Ehsani, S., Ghodsi, M., Haghpanah, N., Immorlica, N., Mahini, H., Mirrokni, V.S.: Equilibrium pricing with positive externalities. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 424–431. Springer, Heidelberg (2010)
Candogan, O., Bimpikis, K., Ozdaglar, A.: Optimal pricing in the presence of local network effects. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 118–132. Springer, Heidelberg (2010)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD 2001, pp. 57–66. ACM, New York (2001)
Hartline, J., Mirrokni, V.S., Sundararajan, M.: Optimal marketing strategies over social networks. In: WWW, pp. 189–198 (2008)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD 2003, pp. 137–146. ACM, New York (2003)
Kleinberg, J.: Cascading behavior in networks: algorithmic and economic issues. Cambridge University Press, Cambridge (2007)
Elchanan, M., Sebastien, R.: On the submodularity of influence in social networks. In: STOC 2007, pp. 128–134. ACM, New York (2007)
Oliver, R.L., Shor, M.: Digital redemption of coupons: Satisfying and dissatisfying e®ects of promotion codes. Journal of Product and Brand Management 12, 121–134 (2003)
Oswald, E.: http://www.betanews.com/article/Google_Buy_MySpace_Ads_for_900m/1155050350
Seeyle, K.Q.: http://www.nytimes.com/2006/08/23/technology/23soft.html
Walker, R.: http://www.slate.com/id/1006264/
Weber, T.: http://news.bbc.co.uk/1/hi/business/6305957.stm?lsf
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Akhlaghpour, H., Ghodsi, M., Haghpanah, N., Mirrokni, V.S., Mahini, H., Nikzad, A. (2010). Optimal Iterative Pricing over Social Networks (Extended Abstract). In: Saberi, A. (eds) Internet and Network Economics. WINE 2010. Lecture Notes in Computer Science, vol 6484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17572-5_34
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DOI: https://doi.org/10.1007/978-3-642-17572-5_34
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