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Impedance coupling in content-targeted advertising

Published: 15 August 2005 Publication History

Abstract

The current boom of the Web is associated with the revenues originated from on-line advertising. While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important. In this work, we study the problem of associating ads with a Web page, referred to as content-targeted advertising, from a computer science perspective. We assume that we have access to the text of the Web page, the keywords declared by an advertiser, and a text associated with the advertiser's business. Using no other information and operating in fully automatic fashion, we propose ten strategies for solving the problem and evaluate their effectiveness. Our methods indicate that a matching strategy that takes into account the semantics of the problem (referred to as AAK for "ads and keywords") can yield gains in average precision figures of 60% compared to a trivial vector-based strategy. Further, a more sophisticated impedance coupling strategy, which expands the text of the Web page to reduce vocabulary impedance with regard to an advertisement, can yield extra gains in average precision of 50%. These are first results. They suggest that great accuracy in content-targeted advertising can be attained with appropriate algorithms.

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      cover image ACM Conferences
      SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
      August 2005
      708 pages
      ISBN:1595930345
      DOI:10.1145/1076034
      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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      Published: 15 August 2005

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      Author Tags

      1. Bayesian networks
      2. advertising
      3. kNN
      4. web

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      • (2021)Exploring Gaze-Based Prediction Strategies for Preference Detection in Dynamic Interface ElementsProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446013(129-139)Online publication date: 14-Mar-2021
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