User behavior modeling is essential in computational advertisement, which builds users' profiles by tracking their online behaviors and then delivers the relevant ads according to each user's interests and needs. Accurate models will lead to higher targeting accuracy and thus improved advertising performance. Intuitively, similar users tend to have similar behaviors towards the displayed ads (e.g., impression, click, conversion). However, to the best of our knowledge, there is not much previous work that explicitly investigates such similarities of various types of user behaviors, and incorporates them into ad response targeting and prediction, largely due to the prohibitive scale of the problem. To bridge this gap, in this paper, we use bipartite graphs to represent historical user behaviors, which consist of both user nodes and advertiser campaign nodes, as well as edges reflecting various types of user-campaign interactions in the past. Based on this representation, we study random-walk-based local algorithms for user behavior modeling and action prediction, whose computational complexity depends only on the size of the output cluster, rather than the entire graph. Our goal is to improve action prediction by leveraging historical user-user, campaign-campaign, and user-campaign interactions. In particular, we propose the bipartite graphs AdvUserGraph accompanied with the ADNI algorithm. ADNI extends the NIBBLE algorithm to AdvUserGraph, and it is able tofi nd the local cluster consisting of interested users towards a specific advertiser campaign. We also propose two extensions of ADNI with improved efficiencies. The performance of the proposed algorithms is demonstrated on both synthetic data and a world leading Demand Side Platform (DSP), showing that they are able to discriminate extremely rare events in terms of their action propensity.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages9
VolumePart F129685
ISBN (Electronic)9781450348874
StatePublished - Aug 13 2017
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017


Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017


  • Computational advertisement
  • Large scale
  • Local graph algorithm
  • User action prediction

ASJC Scopus subject areas

  • Software
  • Information Systems

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