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Customer targeting models using actively-selected web content

Published: 24 August 2008 Publication History

Abstract

We consider the problem of predicting the likelihood that a company will purchase a new product from a seller. The statistical models we have developed at IBM for this purpose rely on historical transaction data coupled with structured firmographic information like the company revenue, number of employees and so on. In this paper, we extend this methodology to include additional text-based features based on analysis of the content on each company's website. Empirical results demonstrate that incorporating such web content can significantly improve customer targeting. Furthermore, we present methods to actively select only the web content that is likely to improve our models, while reducing the costs of acquisition and processing.

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    cover image ACM Conferences
    KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2008
    1116 pages
    ISBN:9781605581934
    DOI:10.1145/1401890
    • General Chair:
    • Ying Li,
    • Program Chairs:
    • Bing Liu,
    • Sunita Sarawagi
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 August 2008

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    Author Tags

    1. active feature-value acquisition
    2. active learning
    3. text categorization
    4. web mining

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    KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2013)How effective is targeted advertising?2013 American Control Conference10.1109/ACC.2013.6580780(6014-6021)Online publication date: Jun-2013
    • (2013)New algorithms for budgeted learningMachine Language10.1007/s10994-012-5299-290:1(59-90)Online publication date: 1-Jan-2013
    • (2012)How effective is targeted advertising?Proceedings of the 21st international conference on World Wide Web10.1145/2187836.2187852(111-120)Online publication date: 16-Apr-2012
    • (2010)A machine-learning approach to discovering company home pages4th IEEE International Conference on Digital Ecosystems and Technologies10.1109/DEST.2010.5610621(361-366)Online publication date: Apr-2010
    • (2010)Medical data miningData Mining and Knowledge Discovery10.1007/s10618-009-0158-x20:3(439-468)Online publication date: 1-May-2010
    • (undefined)How Effective is Targeted Advertising?SSRN Electronic Journal10.2139/ssrn.2242311

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