research-article

Finding keyword from online broadcasting content for targeted advertising

Authors:

Zheng ChenAuthors Info & Claims

ADKDD '07: Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising

Pages 55 - 62

https://doi.org/10.1145/1348599.1348608

Published: 12 August 2007 Publication History

Abstract

Content targeted advertising has been a successful way of delivering ads, as effective technologies were developed to find keywords from the webpage a user is browsing. However, existing technologies cannot be easily applied to find keywords from online broadcasting content, which usually contain more specific phrases and wordings in certain communities than in general webpage content. In this paper, motivated by some existing research works on information extraction field, we suggest a sequential pattern mining-based method to discover language patterns from online broadcasting content. With selected keyword seeds, iteratively applying the language pattern mining and keyword extraction, the proposed technique avoids any tedious labeling work for this task. Our experiments on real-world data show that the proposed keyword extraction algorithm significantly outperforms the baseline method.

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Cited By

Deepika CDheeraj CKartik AVarun BThunuguntla S(2023)User Categorization for Targeted Advertising Using Deep Learning2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353290(1-5)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353290
Srinivasulu AChowdappa KBabu MReddy LVijay Kumar A(2023)Online Advertising Dataset Using ANN (Artificial Neural Networks) and LR (Linear Regression Techniques)Intelligent Computing and Communication10.1007/978-981-99-1588-0_7(71-79)Online publication date: 20-Sep-2023
https://doi.org/10.1007/978-981-99-1588-0_7
Zhang HMu XYan HRen LMa J(2023)A survey of online video advertisingWIREs Data Mining and Knowledge Discovery10.1002/widm.148913:2Online publication date: 18-Jan-2023
https://doi.org/10.1002/widm.1489
Show More Cited By

Index Terms

Finding keyword from online broadcasting content for targeted advertising
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

ADKDD '07: Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising

August 2007

75 pages

ISBN:9781595938336

DOI:10.1145/1348599

General Chair:
Ying Li
adCenter Labs, Microsoft Corporation

Copyright © 2007 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: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12, 2007

California, San Jose

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Overall Acceptance Rate 12 of 21 submissions, 57%

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Cited By

Deepika CDheeraj CKartik AVarun BThunuguntla S(2023)User Categorization for Targeted Advertising Using Deep Learning2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353290(1-5)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353290
Srinivasulu AChowdappa KBabu MReddy LVijay Kumar A(2023)Online Advertising Dataset Using ANN (Artificial Neural Networks) and LR (Linear Regression Techniques)Intelligent Computing and Communication10.1007/978-981-99-1588-0_7(71-79)Online publication date: 20-Sep-2023
https://doi.org/10.1007/978-981-99-1588-0_7
Zhang HMu XYan HRen LMa J(2023)A survey of online video advertisingWIREs Data Mining and Knowledge Discovery10.1002/widm.148913:2Online publication date: 18-Jan-2023
https://doi.org/10.1002/widm.1489
Datta SMali KDas SKundu SHarh S(2022)Rhythmus periodic frequent pattern mining without periodicity thresholdJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03617-814:7(8551-8563)Online publication date: 13-Jan-2022
https://doi.org/10.1007/s12652-021-03617-8
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
Hong TChoi JLim KKim P(2020)Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural NetworksSensors10.3390/s2101019921:1(199)Online publication date: 30-Dec-2020
https://doi.org/10.3390/s21010199
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
Nyandag BLi RDemberel O(2017)Keyword extraction based on statistical information for cyrillic Mongolian script2017 29th Chinese Control And Decision Conference (CCDC)10.1109/CCDC.2017.7978889(2250-2255)Online publication date: May-2017
https://doi.org/10.1109/CCDC.2017.7978889
Ferreira ROkada KCristo MBerlt Kde Moura EFernandes DBressan GMelo Silveira RMunson ESantanchà Ada Graèa Campos Pimentel M(2012)Scripts as source of information to contextual video advertisingProceedings of the 18th Brazilian symposium on Multimedia and the web10.1145/2382636.2382647(39-46)Online publication date: 15-Oct-2012
https://dl.acm.org/doi/10.1145/2382636.2382647
Mei TLi LHua XLi S(2012)ImageSenseACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2071396.20714028:1(1-18)Online publication date: 3-Feb-2012
https://dl.acm.org/doi/10.1145/2071396.2071402
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