research-article

Automatic generation of bid phrases for online advertising

Authors:

Evgeniy Gabrilovich,

Vanja Josifovski,

Sandeep Pandey,

Bo PangAuthors Info & Claims

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

Pages 341 - 350

https://doi.org/10.1145/1718487.1718530

Published: 04 February 2010 Publication History

Abstract

One of the most prevalent online advertising methods is textual advertising. To produce a textual ad, an advertiser must craft a short creative (the text of the ad) linking to a landing page, which describes the product or service being promoted. Furthermore, the advertiser must associate the creative to a set of manually chosen bid phrases representing those Web search queries that should trigger the ad. For efficiency, given a landing page, the bid phrases are often chosen first, and then for each bid phrase the creative is produced using a template. Nevertheless, an ad campaign (e.g., for a large retailer) might involve thousands of landing pages and tens or hundreds of thousands of bid phrases, hence the entire process is very laborious.

Our study aims towards the automatic construction of online ad campaigns: given a landing page, we propose several algorithmic methods to generate bid phrases suitable for the given input. Such phrases must be both relevant (that is, reflect the content of the page) and well-formed (that is, likely to be used as queries to a Web search engine). To this end, we use a two phase approach. First, candidate bid phrases are generated by a number of methods, including a (mono-lingual) translation model capable of generating phrases contained within the text of the input as well as previously "unseen" phrases. Second, the candidates are ranked in a probabilistic framework using both the translation model, which favors relevant phrases, as well as a bid phrase language model, which favors well-formed phrases.

Empirical evaluation based on a real-life corpus of advertiser-created landing pages and associated bid phrases confirms the value of our approach, which successfully re-generates many of the human-crafted bid phrases and performs significantly better than a pure text extraction method.

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Cited By

Terzioğlu SÇoğalmış KBulut A(2022)Ad creative generation using reinforced generative adversarial networkElectronic Commerce Research10.1007/s10660-022-09564-6Online publication date: 5-May-2022
https://doi.org/10.1007/s10660-022-09564-6
Prabhu YKusupati AGupta NVarma MCaverlee JHu XLalmas MWang W(2020)Extreme Regression for Dynamic Search AdvertisingProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371768(456-464)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371768
Zhou HHuang MMao YZhu CShu PZhu X(2019)Domain-Constrained Advertising Keyword GenerationThe World Wide Web Conference10.1145/3308558.3313570(2448-2459)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313570
Show More Cited By

Index Terms

Automatic generation of bid phrases for online advertising
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

February 2010

468 pages

ISBN:9781605588896

DOI:10.1145/1718487

General Chairs:
Brian D. Davison
Lehigh University, USA
,
Torsten Suel
Polytechnic Institute of NYU, USA
,
Program Chairs:
Nick Craswell
Microsoft, USA
,
Bing Liu
University of Illinois, Chicago, USA

Copyright © 2010 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 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: 04 February 2010

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February 4 - 6, 2010

New York, New York, USA

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Cited By

Terzioğlu SÇoğalmış KBulut A(2022)Ad creative generation using reinforced generative adversarial networkElectronic Commerce Research10.1007/s10660-022-09564-6Online publication date: 5-May-2022
https://doi.org/10.1007/s10660-022-09564-6
Prabhu YKusupati AGupta NVarma MCaverlee JHu XLalmas MWang W(2020)Extreme Regression for Dynamic Search AdvertisingProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371768(456-464)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371768
Zhou HHuang MMao YZhu CShu PZhu X(2019)Domain-Constrained Advertising Keyword GenerationThe World Wide Web Conference10.1145/3308558.3313570(2448-2459)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313570
Yang XSun DZhu RDeng TGuo ZDing ZQin SZhu YTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)AiAdsProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330782(1881-1890)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330782
Zhao WZhang BWang BGuan ZGuan WQiu GNing WChen JLiu HTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Personalized Attraction Enhanced Sponsored Search with Multi-task LearningProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330659(2632-2642)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330659
Yang YJansen BYang YGuo XZeng D(2019)Keyword Optimization in Sponsored Search AdvertisingIEEE Intelligent Systems10.1109/MIS.2019.289359034:1(32-42)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/MIS.2019.2893590
Prabhu YKag AHarsola SAgrawal RVarma MChampin PGandon FMédini LLalmas MIpeirotis P(2018)ParabelProceedings of the 2018 World Wide Web Conference10.1145/3178876.3185998(993-1002)Online publication date: 10-Apr-2018
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Qin TChen WLiu T(2015)Sponsored Search AuctionsACM Transactions on Intelligent Systems and Technology10.1145/26681085:4(1-34)Online publication date: 23-Jan-2015
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Gkorgkas OVlachou ADoulkeridis CNørvåg K(2015)Maximizing Influence of Spatio-Textual Objects Based on Keyword SelectionAdvances in Spatial and Temporal Databases10.1007/978-3-319-22363-6_22(413-430)Online publication date: 13-Aug-2015
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Bulut A(2014)TopicMachine: Conversion Prediction in Search Advertising Using Latent Topic ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.231386826:11(2846-2858)Online publication date: 1-Nov-2014
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