document: Mining advertiser-specific user behavior using adfactors Consider an online ad campaign run by an advertiser . The ad serving companies that handle such campaigns record users ' behavior that leads to impressions of campaign ads , as well as users ' responses to such impressions . This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions . The most popular reporting statistics are the click-through rate and the conversion rate . While these are indicative of the effectiveness of an ad campaign , the advertisers often seek to understand more sophisticated long-term effects of their ads on the brand awareness and the user behavior that leads to the conversion , thus creating a need for the reporting measures that can capture both the duration and the frequency of the pathways to user conversions . In this paper , we propose an alternative data mining framework for analyzing user-level advertising data . In the aggregation step , we compress individual user histories into a graph structure , called the adgraph , representing local correlations between ad events . For the reporting step , we introduce several scoring rules , called the adfactors ( AF ) , that can capture global role of ads and ad paths in the adgraph , in particular , the structural correlation between an ad impression and the user conversion . We present scalable local algorithms for computing the adfactors ; all algorithms were implemented using the MapReduce programming model and the Pregel framework . Using an anonymous user-level dataset of sponsored search campaigns for eight different advertisers , we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data , and show its value for mining the long-term behavioral patterns in the advertising data .
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