A Sub-linear, Massive-scale Look-alike Audience Extension System A Massive-scale Look-alike Audience Extension

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Qiang Ma, Musen Wen, Zhen Xia, Datong Chen ;
Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications at KDD 2016, PMLR 53:51-67, 2016.

Abstract

Look-alike audience extension is a practically effective way to customize high-performance audience in on-line advertising. With look-alike audience extension system, any advertiser can easily generate a set of customized audience by just providing a list of existing customers without knowing the detailed targetable attributes in a sophisticated advertising system. In this paper, we present our newly developed graph-based look-alike system in Yahoo! advertising platform which provides look-alike audiences for thousands of campaigns. Extensive experiments have been conducted to compare our look-alike model with three other existing look-alike systems using billions of users and millions of user features. The experiment results show that our developed graph-based method with nearest-neighbor filtering outperforms other methods by more than 50% regarding conversion rate in app-install ad campaigns.

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