research-article

Learning from multi-topic web documents for contextual advertisement

Authors:

Arun C. Surendran,

Mukund NarasimhanAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1051 - 1059

https://doi.org/10.1145/1401890.1402015

Published: 24 August 2008 Publication History

Abstract

Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges. Often advertisers wish to either target (or avoid) some specific content on web pages which may appear only in a small part of the page. Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training. In this paper we investigate ways to learn for sub-document classification when only page level labels are available - these labels only indicate if the relevant content exists in the given page or not. We propose the application of multiple-instance learning to this task to improve the effectiveness of traditional methods. We apply sub-document classification to two different problems in contextual advertising. One is "sensitive content detection" where the advertiser wants to avoid content relating to war, violence, pornography, etc. even if they occur only in a small part of a page. The second problem involves opinion mining from review sites - the advertiser wants to detect and avoid negative opinion about their product when positive, negative and neutral sentiments co-exist on a page. In both these scenarios we present experimental results to show that our proposed system is able to get good block level labeling for free and improve the performance of traditional learning methods.

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Zhang JWu YHao FLiu XLi MZhou DZheng W(2024)Double similarities weighted multi-instance learning kernel and its applicationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121900238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121900
Alekseev ANikolenko SKabaeva G(2024)Benchmarking Multilabel Topic Classification in the Kyrgyz LanguageAnalysis of Images, Social Networks and Texts10.1007/978-3-031-54534-4_2(21-35)Online publication date: 12-Mar-2024
https://doi.org/10.1007/978-3-031-54534-4_2
Show More Cited By

Index Terms

Learning from multi-topic web documents for contextual advertisement
1. Information systems
  1. Information systems applications

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Information

Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 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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Publication History

Published: 24 August 2008

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KDD08: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2008

Nevada, Las Vegas, USA

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

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

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Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Zhang JWu YHao FLiu XLi MZhou DZheng W(2024)Double similarities weighted multi-instance learning kernel and its applicationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121900238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121900
Alekseev ANikolenko SKabaeva G(2024)Benchmarking Multilabel Topic Classification in the Kyrgyz LanguageAnalysis of Images, Social Networks and Texts10.1007/978-3-031-54534-4_2(21-35)Online publication date: 12-Mar-2024
https://doi.org/10.1007/978-3-031-54534-4_2
Fatima SAli SKim H(2023)A Comprehensive Review on Multiple Instance LearningElectronics10.3390/electronics1220432312:20(4323)Online publication date: 18-Oct-2023
https://doi.org/10.3390/electronics12204323
Liu WWang L(2023)Natural-Annotation-Based Malay Multiword Expressions Extraction and ClusteringComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-23793-5_13(143-152)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-23793-5_13
Romeo LCavallo APepa LBianchi-Berthouze NPontil M(2022)Multiple Instance Learning for Emotion Recognition Using Physiological SignalsIEEE Transactions on Affective Computing10.1109/TAFFC.2019.295411813:1(389-407)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TAFFC.2019.2954118
Bhagat HSingh M(2022)Machine‐Learning and Deep‐Learning Techniques in Social SciencesMachine Learning Algorithms for Signal and Image Processing10.1002/9781119861850.ch23(409-428)Online publication date: 18-Nov-2022
https://doi.org/10.1002/9781119861850.ch23
Nunna SBhattu SSomayajulu DKumar N(2021)Structure-sensitive graph-based multiple-instance semi-supervised learningSādhanā10.1007/s12046-021-01659-446:3Online publication date: 3-Aug-2021
https://doi.org/10.1007/s12046-021-01659-4
Choi JLim K(2020)Identifying machine learning techniques for classification of target advertisingICT Express10.1016/j.icte.2020.04.0126:3(175-180)Online publication date: Sep-2020
https://doi.org/10.1016/j.icte.2020.04.012
Leonardo RHu AUzair MLu QFu INishiyama KMangalath Subrahmannian SRavichandran D(2019)Fusing Visual and Textual Information to Determine Content Safety2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)10.1109/ICMLA.2019.00324(2026-2031)Online publication date: Dec-2019
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