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Translating relevance scores to probabilities for contextual advertising

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Rajiv KhannaAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1899 - 1902

https://doi.org/10.1145/1645953.1646260

Published: 02 November 2009 Publication History

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Abstract

Information retrieval systems conventionally assess document relevance using the bag of words model. Consequently, relevance scores of documents retrieved for different queries are often difficult to compare, as they are computed on different (or even disjoint) sets of textual features. Many tasks, such as federation of search results or global thresholding of relevance scores, require that scores be globally comparable. To achieve this, in this paper we propose methods for non-monotonic transformation of relevance scores into probabilities for a contextual advertising selection engine that uses a vector space model. The calibration of the raw scores is based on historical click data.

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Cited By

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Pinar MAktas AUlku E(2025)Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and MLComputers in Biology and Medicine10.1016/j.compbiomed.2024.109603186(109603)Online publication date: Mar-2025
Chaudhuri STheocharous GGhavamzadeh M(2017)Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement DisplayMachine Learning and Data Mining in Pattern Recognition10.1007/978-3-319-62416-7_3(32-46)Online publication date: 2-Jul-2017
Long BChang Y(2014)Relevance Ranking for Vertical Search EnginesundefinedOnline publication date: 14-Feb-2014
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Index Terms

Translating relevance scores to probabilities for contextual advertising
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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: 02 November 2009

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Pinar MAktas AUlku E(2025)Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and MLComputers in Biology and Medicine10.1016/j.compbiomed.2024.109603186(109603)Online publication date: Mar-2025
Chaudhuri STheocharous GGhavamzadeh M(2017)Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement DisplayMachine Learning and Data Mining in Pattern Recognition10.1007/978-3-319-62416-7_3(32-46)Online publication date: 2-Jul-2017
Long BChang Y(2014)Relevance Ranking for Vertical Search EnginesundefinedOnline publication date: 14-Feb-2014
Zhao KKumar A(2013)Who blogs whatWorld Wide Web10.1007/s11280-012-0167-316:5-6(621-644)Online publication date: 1-Nov-2013
Chung SGoswami ALee HHu JBaughman AGao JPan JChu FWang Y(2012)The impact of images on user clicks in product searchProceedings of the Twelfth International Workshop on Multimedia Data Mining10.1145/2343862.2343866(25-33)Online publication date: 12-Aug-2012
Cetintas SChen DSi L(2012)Forecasting user visits for online display advertisingInformation Retrieval10.1007/s10791-012-9201-416:3(369-390)Online publication date: 30-May-2012
Li HBhowmick SSun A(2011)AffRank: Affinity-driven ranking of products in online social rating networksJournal of the American Society for Information Science and Technology10.1002/asi.2155562:7(1345-1359)Online publication date: 1-Jul-2011

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Where to stop reading a ranked list?: threshold optimization using truncated score distributions

Displaying relevance scores for search results

Relevance information: a loss of entropy but a gain for IDF?

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