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Information retrieval challenges in computational advertising

Published: 19 July 2010 Publication History

Abstract

Computational advertising is an emerging scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics. The central challenge of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The aim of this tutorial is to present the state of the art in Computational Advertising, in particular in its IR-related aspects, and to expose the participants to the current research challenges in this field. The tutorial does not assume any prior knowledge of Web advertising, and will begin with a comprehensive background survey. Going deeper, our focus will be on using a textual representation of the user context to retrieve relevant ads. At first approximation, this process can be reduced to a conventional setup by constructing a query that describes the user context and executing the query against a large inverted index of ads. We show how to augment this approach using query expansion and text classification techniques tuned for the ad-retrieval problem. In particular, we show how to use the Web as a repository of query-specific knowledge and use the Web search results retrieved by the query as a form of a relevance feedback and query expansion. We also present solutions that go beyond the conventional bag of words indexing by constructing additional features using a large external taxonomy and a lexicon of named entities obtained by analyzing the entire Web as a corpus. The last part of the tutorial will be devoted to a potpourri of recent research results and open problems inspired by Computational Advertising challenges in text summarization, natural language generation, named entity recognition, computer-human interaction, and other SIGIR-relevant areas.

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 19 July 2010

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    Author Tags

    1. content match
    2. online advertising
    3. sponsored search

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    SIGIR '10
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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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