Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)
Abstract
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 -anonymous cohort building algorithm called {\em consecutive consistent weighted sampling} (CCWS). The proposed method combines the spirit of the (-powered) consistent weighted sampling and hierarchical clustering, so that the -anonymity is ensured by enforcing a lower bound on the size of cohorts. Evaluations on a LinkedIn dataset consisting of M 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.
Cite
@article{arxiv.2304.13677,
title = {Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)},
author = {Xinyi Zheng and Weijie Zhao and Xiaoyun Li and Ping Li},
journal= {arXiv preprint arXiv:2304.13677},
year = {2023}
}