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Information graph model and application to online advertising

Published: 01 November 2013 Publication History

Abstract

We present an algorithm which adapts a graph-based ranking model to the context of the problem of improving the process of serving advertisements to users. We transform the ad-based clickstream data into a heterogeneous graph model which respects differences in feature types (e.g. geolocation features, or browser-history features). The heterogeneous network model generates meaningful rankings of features which are predictive for each ad, as demonstrated by our classifier's performance. We also discuss how, in addition to serving as the basis for a classifier, this model may also provide an informative view of the data, which is not possible with black-box approaches, and which therefore makes it very suitable to the problem space of targeted ad serving.

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Cited By

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  • (2023)Partitioning-Aware Performance Modeling of Distributed Graph Processing TasksInternational Journal of Parallel Programming10.1007/s10766-023-00753-w51:4-5(231-255)Online publication date: 5-May-2023
  • (2015)k-Means Clustering on Two-Level Memory SystemsProceedings of the 2015 International Symposium on Memory Systems10.1145/2818950.2818977(197-205)Online publication date: 5-Oct-2015

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    cover image ACM Conferences
    UEO '13: Proceedings of the 1st workshop on User engagement optimization
    November 2013
    36 pages
    ISBN:9781450324212
    DOI:10.1145/2512875
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    Publication History

    Published: 01 November 2013

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    Author Tags

    1. feature ranking
    2. information network
    3. targeted advertising

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    UEO '13 Paper Acceptance Rate 6 of 6 submissions, 100%;
    Overall Acceptance Rate 6 of 6 submissions, 100%

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    • (2023)Partitioning-Aware Performance Modeling of Distributed Graph Processing TasksInternational Journal of Parallel Programming10.1007/s10766-023-00753-w51:4-5(231-255)Online publication date: 5-May-2023
    • (2015)k-Means Clustering on Two-Level Memory SystemsProceedings of the 2015 International Symposium on Memory Systems10.1145/2818950.2818977(197-205)Online publication date: 5-Oct-2015

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