This document discusses using graph-based machine learning on browsing history data to discover customer purchase intent for advertisers. It presents challenges with existing solutions like SVD that identify general online buyers but not advertiser-specific patterns. The document proposes representing sites as a graph and using GraphX's Pregel API to propagate positive customer labels along site connections, assigning higher scores to similar sites. Evaluation shows this approach identifies advertiser-relevant sites while addressing issues like model sparsity and frequency. It also provides lessons learned on optimizing Spark jobs.