Mining advertiser-specific user behavior using adfactors

Nikolay Archak, Vahab S. Mirrokni, S. Muthukrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Pages31-40
Number of pages10
DOIs
StatePublished - Jul 20 2010
Externally publishedYes
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: Apr 26 2010Apr 30 2010

Publication series

NameProceedings of the 19th International Conference on World Wide Web, WWW '10

Other

Other19th International World Wide Web Conference, WWW2010
CountryUnited States
CityRaleigh, NC
Period4/26/104/30/10

Keywords

  • ad auctions
  • clickthrough rate
  • conversion rate
  • online advertising
  • pagerank
  • sponsored search
  • user behavior models

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Archak, N., Mirrokni, V. S., & Muthukrishnan, S. (2010). Mining advertiser-specific user behavior using adfactors. In Proceedings of the 19th International Conference on World Wide Web, WWW '10 (pp. 31-40). (Proceedings of the 19th International Conference on World Wide Web, WWW '10). https://doi.org/10.1145/1772690.1772695