poster

Modeling click and relevance relationship for sponsored search

Authors:

Wei Vivian Zhang,

Ye Chen,

Mitali Gupta,

Swaraj Sett,

Tak W. YanAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 119 - 120

https://doi.org/10.1145/2487788.2487844

Published: 13 May 2013 Publication History

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Abstract

Click-through rate (CTR) prediction and relevance ranking are two fundamental problems in web advertising. In this study, we address the problem of modeling the relationship between CTR and relevance for sponsored search. We used normalized relevance scores comparable across all queries to represent relevance when modeling with CTR, instead of directly using human judgment labels or relevance scores valid only within same query. We classified clicks by identifying their relevance quality using dwell time and session information, and compared all clicks versus selective clicks effects when modeling relevance.

Our results showed that the cleaned click signal outperforms raw click signal and others we explored, in terms of relevance score fitting. The cleaned clicks include clicks with dwell time greater than 5 seconds and last clicks in session. Besides traditional thoughts that there is no linear relation between click and relevance, we showed that the cleaned click based CTR can be fitted well with the normalized relevance scores using a quadratic regression model. This relevance-click model could help to train ranking models using processed click feedback to complement expensive human editorial relevance labels, or better leverage relevance signals in CTR prediction.

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Y. Chen and T. W. Yan. Position-normalized click prediction in search advertising. In KDD, pages 795--803, 2012.

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Cited By

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Yang YYang YJansen BLalmas M(2017)Computational Advertising: A Paradigm Shift for Advertising and Marketing?IEEE Intelligent Systems10.1109/MIS.2017.5832:3(3-6)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/MIS.2017.58
Çogalmis KSagoglu OBulut A(2017)AdScope: Search Campaign Scoping Using Relevance FeedbackIEEE Intelligent Systems10.1109/MIS.2017.4732:3(14-20)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/MIS.2017.47

Index Terms

Modeling click and relevance relationship for sponsored search
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Yang YYang YJansen BLalmas M(2017)Computational Advertising: A Paradigm Shift for Advertising and Marketing?IEEE Intelligent Systems10.1109/MIS.2017.5832:3(3-6)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/MIS.2017.58
Çogalmis KSagoglu OBulut A(2017)AdScope: Search Campaign Scoping Using Relevance FeedbackIEEE Intelligent Systems10.1109/MIS.2017.4732:3(14-20)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/MIS.2017.47

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Investigating the relevance of sponsored results for web ecommerce queries

Online learning from click data for sponsored search

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