Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS)
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To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such identity regime, an accurate and efficient cohort building algorithm is desired to group users with similar characteristics. In this paper, we propose a scalable K-anonymous cohort building algorithm called consecutive consistent weighted sampling (CCWS). The proposed method combines the spirit of the (p-powered) consistent weighted sampling and hierarchical clustering, so that the K-anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of >70M users and ads campaigns demonstrate that CCWS achieves substantial improvements over several hashing-based methods including sign random projections (SignRP), minwise hashing (MinHash), as well as the vanilla CWS.
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