Publications

Interactive Audience Expansion On Large Scale Online Visitor Data

ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Published August 14, 2021

Gromit Yeuk-Yin Chan, Tung Mai, Anup Rao, Ryan A. Rossi, Fan Du, Claudio T. Silva, Juliana Freire

Online marketing platforms often store millions of website visitors' behavior as a large sparse matrix with rows as visitors and columns as behavior. These platforms allow marketers to conduct Audience Expansion, a technique to identify new audiences with similar behavior to the original target audiences. In this paper, we propose a method to achieve interactive Audience Expansion from millions of visitor data efficiently. Unlike other methods that undergo significant computations upon inputs, our approach provides interactive responses when a marketer inputs the target audiences and similarity measures. The idea is to apply data summarization technique on the large visitor matrix to obtain a small set of summaries representing the similarities in the matrix. We propose efficient algorithms to compute the data summaries on a distributed computing environment (i.e., Spark) and conduct the expansion using the summaries. Our experiment shows that our approach (1) provides 10 times more accurate and 27 times faster Audience Expansion results on real datasets and (2) achieves a 98% speed-up compared to straightforward data summarization implementations. We also present an interface to apply the algorithm for real-world scenarios.

Learn More

Research Area:  AI & Machine Learning