Compact Web browsing profiles for click-through rate prediction

Bjarne Ørum Fruergaard, Lars Kai Hansen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In real time advertising we are interested in finding features that improve click-through rate prediction. One source of available information is the bipartite graph of websites previously engaged by identifiable users. In this work, we investigate three different decompositions of such a graph with varying degrees of sparsity in the representations. The decompositions that we consider are SVD, NMF, and IRM. To quantify the utility, we measure the performances of these representations when used as features in a sparse logistic regression model for click-through rate prediction. We recommend the IRM bipartite clustering features as they provide the most compact representation of browsing patterns and yield the best performance.
Original languageEnglish
Title of host publicationProceedings of IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
PublisherIEEE
Publication date2014
Pages1-6
ISBN (Electronic)9781479936946
DOIs
Publication statusPublished - 2014
Event24th IEEE International Workshop on Machine Learning for Signal Processing - Reims Centre des Congrès, Reims, France
Duration: 21 Sep 201424 Sep 2014
Conference number: 24
http://mlsp2014.conwiz.dk/home.htm
http://mlsp2014.conwiz.dk/home.htm

Conference

Conference24th IEEE International Workshop on Machine Learning for Signal Processing
Number24
LocationReims Centre des Congrès
CountryFrance
CityReims
Period21/09/201424/09/2014
Internet address

Keywords

  • Bioengineering
  • Communication, Networking and Broadcast Technologies
  • Computing and Processing
  • Engineering Profession
  • Signal Processing and Analysis
  • Computational modeling
  • Data models
  • Logistics
  • Matrix decomposition
  • Predictive models
  • Uniform resource locators
  • Vectors

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