research-article

Interpreting Advertiser Intent in Sponsored Search

Authors:

Bhanu C. Vattikonda,

Santhosh Kodipaka,

Alex C. SnoerenAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2177 - 2185

https://doi.org/10.1145/2783258.2788566

Published: 10 August 2015 Publication History

Abstract

Search engines derive revenue by displaying sponsored results along with organic results in response to user queries. In general, search engines run a per-query, on-line auction amongst interested advertisers to select sponsored results to display. In doing so, they must carefully balance the revenue derived from sponsored results against potential degradation in user experience due to less-relevant results. Hence, major search engines attempt to analyze the relevance of potential sponsored results to the user's query using supervised learning algorithms. Past work has employed a bag-of-words approach using features extracted from both the query and potential sponsored result to train the ranker.

We show that using features that capture the advertiser's intent can significantly improve the performance of relevance ranking. In particular, we consider the ad keyword the advertiser submits as part of the auction process as a direct expression of intent. We leverage the search engine itself to interpret the ad keyword by submitting the ad keyword as an independent query and incorporating the results as features when determining the relevance of the advertiser's sponsored result to the user's original query. We achieve 43.2% improvement in precision-recall AUC over the best previously published baseline and 2.7% improvement in the production system of a large search engine.

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Cited By

Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
DeBlasio JGuha SVoelker GSnoeren AUhlig SMaennel O(2017)Exploring the dynamics of search advertiser fraudProceedings of the 2017 Internet Measurement Conference10.1145/3131365.3131393(157-170)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1145/3131365.3131393
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
Show More Cited By

Index Terms

Interpreting Advertiser Intent in Sponsored Search
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

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 the author(s) 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: 10 August 2015

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National Science Foundation

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
DeBlasio JGuha SVoelker GSnoeren AUhlig SMaennel O(2017)Exploring the dynamics of search advertiser fraudProceedings of the 2017 Internet Measurement Conference10.1145/3131365.3131393(157-170)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1145/3131365.3131393
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
Bauer SRadlinski FWhite RBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Where Can I Buy a Boulder?Proceedings of the 25th International Conference on World Wide Web10.1145/2872427.2882998(1225-1235)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2882998

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