Social Network Ad Allocation via Hyperbolic Embedding
Peixin Gao, Hui Miao and John S. Baras
To appear in the Proceedings of the 53rd IEEE Conference on Decision Control (CDC 2014), Los Angeles, CA, 15-17 December, 2014.
With the increasing popularity and ubiquity of online social networks (SNS), many advertisers choose to post their advertisements within SNS, and the problem of advertisements allocation in SNS has received increasing attention from both industry and academia. Ad Allocation, as a central problem for SNS Ad platforms, is to maximize the revenue of an SNS Ad platform without overcharging advertisers. The offline version of the problem is a high dimensional integer programming problem with constraints incorporating potential allocation requirement by advertisers. In this paper we investigate the SNS advertising allocation problem in a homogeneous setting, study the connection of SNS advertising and hyperbolic geometry, and propose an approximation using hyperbolic embedding, which not only reduces the dimensionality of SNS Ad allocation problem significantly, but also provides a general framework for designing allocation strategies incorporating business rules. We evaluate the optimality and efficiency of our approach.