abstract

Computational advertising: leveraging user interaction & contextual factors for improved ad relevance & targeting

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Kushal S. DaveAuthors Info & Claims

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

Pages 757 - 758

https://doi.org/10.1145/2124295.2124387

Published: 08 February 2012 Publication History

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Abstract

Computational advertising refers to finding the most relevant ads matching a particular context on the web. The core problem attacked in computational advertising CA is of the match making between the ads and the context. My research work aims at leveraging various user interaction, ad and advertiser related information and contextual information for improving the relevance, ranking and targeting of ads. The research work focuses on the identification of various factors that contribute in retrieving and ranking the most relevant set of ads that match best with the context. Specifically, information associated with the user, publisher and advertiser is leveraged for this purpose.

References

[1]

K. S. Dave, R. Bhatt, and V. Varma. Modeling action cascades in social networks. ICWSM '11. AAAI, 2011.

Google Scholar

[2]

K. S. Dave and V. Varma. Learning the click-through rate for rare/new ads from similar ads. SIGIR '10, 2010.

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[3]

K. S. Dave and V. Varma. Pattern based keyword extraction for contextual advertising. In CIKM, pages 1885--1888, 2010.

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Cited By

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Truong VHoang V(2022)Machine Learning Optimization in Computational Advertising—A Systematic Literature ReviewIntelligent Systems Modeling and Simulation II10.1007/978-3-031-04028-3_8(97-111)Online publication date: 13-Oct-2022
https://doi.org/10.1007/978-3-031-04028-3_8
Kristyan SSuhardi Albarda Dabarsyah B(2013)Targeted advertising optimization using vector space model for online behavior on news portal computational advertising case study: Harianjogja.com2013 Joint International Conference on Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T)10.1109/rICT-ICeVT.2013.6741549(1-6)Online publication date: Nov-2013
https://doi.org/10.1109/rICT-ICeVT.2013.6741549

Index Terms

Computational advertising: leveraging user interaction & contextual factors for improved ad relevance & targeting
1. Computing methodologies
  1. Machine learning

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Information

Published In

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

February 2012

792 pages

ISBN:9781450307475

DOI:10.1145/2124295

General Chairs:
Eytan Adar
University of Michigan, USA
,
Jaime Teevan
Microsoft Research, USA
,
Program Chairs:
Eugene Agichtein
Emory University, USA
,
Yoelle Maarek
Yahoo! Research, Israel

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2012

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WSDM'12

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WSDM'12: Fifth ACM International Conference on Web Search and Data Mining

February 8 - 12, 2012

Washington, Seattle, USA

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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Truong VHoang V(2022)Machine Learning Optimization in Computational Advertising—A Systematic Literature ReviewIntelligent Systems Modeling and Simulation II10.1007/978-3-031-04028-3_8(97-111)Online publication date: 13-Oct-2022
https://doi.org/10.1007/978-3-031-04028-3_8
Kristyan SSuhardi Albarda Dabarsyah B(2013)Targeted advertising optimization using vector space model for online behavior on news portal computational advertising case study: Harianjogja.com2013 Joint International Conference on Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T)10.1109/rICT-ICeVT.2013.6741549(1-6)Online publication date: Nov-2013
https://doi.org/10.1109/rICT-ICeVT.2013.6741549

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Computational advertising: leveraging user interaction & contextual factors for improved ad retrieval & ranking

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