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Statistical techniques for online personalized advertising: a survey

Published: 26 March 2012 Publication History

Abstract

Online advertising is the major source of revenue for most web service providers. Displaying advertisements that match user interests will not only lead to user satisfaction, but it will also maximize the revenues of both advertisers and web publishers. Online Advertisement systems use web mining and machine learning techniques to personalize advertisement selection to a particular user based on certain features such as his browsing behavior or demographic data. This paper presents an overview of online advertisement selection and summarizes the main technical challenges and open issues in this field. The paper investigates most of the relevant existing approaches carried out towards this perspective and provides a comparison and classification of these approaches.

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cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca
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: 26 March 2012

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Author Tags

  1. contextual advertising
  2. matching
  3. online advertising
  4. personalization
  5. sponsored Search
  6. web advertising

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SAC 2012
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SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2022)Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival NetworksApplied Sciences10.3390/app1207359412:7(3594)Online publication date: 1-Apr-2022
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  • (2021)Efficient Learning to Learn a Robust CTR Model for Web-scale Online Sponsored Search AdvertisingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481912(4203-4213)Online publication date: 26-Oct-2021
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  • (2017)Logistic Regression with Stochastic Gradient Ascent to Estimate Click Through RateInformation and Communication Technology for Sustainable Development10.1007/978-981-10-3932-4_33(319-326)Online publication date: 8-Nov-2017
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