Cited by 153Recommender Systems and TechniquesDigital Marketing and Social MediaInformation Retrieval and Search Behavior

The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.

Identifying machine learning techniques for classification of target advertising

Open access

Choi, Jin-A, Lim, Kiho·2020·ICT Express

DOI 10.1016/j.icte.2020.04.012

Open this paper in CoChat — free account Open PDF

Abstract

There have been numerous applications of artificial intelligence (AI) technologies to online advertising, especially to optimize the reach of target audiences. Previous studies show that improved computational power significantly advances granular audience targeting capabilities. This study investigates and classifies various machine learning techniques that are used to enhance targeted online advertising. Twenty-three machine learning-based online targeted advertising strategies are identified and classified largely into two categories, user-centric and content-centric approaches. The paper also identifies an underexamined area, algorithm-based detection of click frauds, to illustrate how machine learning approaches can be integrated to preserve the viability of online advertising.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.icte.2020.04.012

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
Simple and Scalable Response Prediction for Display Advertising
Chapelle, O., Manavoglu, E., & Rosales, R. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2532128 · Cited by 392
Real-Time Bidding by Reinforcement Learning in Display Advertising
Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., & Guo, D. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018702 · Cited by 269
Object-Level Video Advertising: An Optimization Framework
Zhang, H., Cao, X., Ho, J. K. L., & Chow, T. W. S. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2605629 · Cited by 190
Machine learning for targeted display advertising: transfer learning in action
Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. J. (2013) · Machine Learning · doi:10.1007/s10994-013-5375-2 · Cited by 160
Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations
Căpățînă, A., Kachour, M., Lichy, J., Micu, A., Micu, A., & Codignola, F. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119794 · Cited by 159
Contextual advertising by combining relevance with click feedback
Chakrabarti, D., Agarwal, D., & Josifovski, V. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367554 · Cited by 149
Sitcom-star-based clothing retrieval for video advertising: a deep learning framework
Zhang, H., Ji, Y., Huang, W., & Liu, L. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3579-x · Cited by 89
An ensemble learning based approach for impression fraud detection in mobile advertising
Haider, C. M. R., Iqbal, A., Rahman, A. H., & Rahman, M. S. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.021 · Cited by 72
An efficient protocol for authenticated and secure message delivery in vehicular ad hoc networks
Lim, K., & Manivannan, D. (2016) · Vehicular Communications · doi:10.1016/j.vehcom.2016.03.001 · Cited by 69
Self-citation

Show all 31 references

Research works printed but not linked: 1

A. Addis, G. Armano, E. Vargiu, Profiling users to perform contextual advertising, in: Proceedings of the 10th Workshop dagli Oggetti agli Agenti (WOA 2009), 2009.

Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023) · Sage Open · doi:10.1177/21582440231210759 · Cited by 244
Applications of artificial intelligence in B2B marketing: Challenges and future directions
Moradi, M., & Dass, M. (2022) · Industrial Marketing Management · doi:10.1016/j.indmarman.2022.10.016 · Cited by 162
From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate
Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71
“…considered sensitive trade secrets, the corresponding machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data being continuously gathered through interactions with digital platforms [19, 20, 31].”
Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda
Suraña‐Sánchez, C., & Aramendia-Muneta, M. E. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13027 · Cited by 77
Exploring the personalization-intrusiveness-intention framework to evaluate the effects of personalization in social media
Lee, J., Kim, C. G., & Lee, K. C. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102532 · Cited by 60
AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal Data
Häglund, E., & Björklund, J. (2024) · Journal of Current Issues & Research in Advertising · doi:10.1080/10641734.2024.2334939 · Cited by 50
Decoding the Relationship of Artificial Intelligence, Advertising, and Generative Models
Park, H. W., Lim, C. V., Zhu, Y. P., & Omar, M. (2024) · Digital · doi:10.3390/digital4010013 · Cited by 54
An Ensemble Architecture Based on Deep Learning Model for Click Fraud Detection in Pay-Per-Click Advertisement Campaign
Batool, A., & Byun, Y.-C. (2022) · IEEE Access · doi:10.1109/access.2022.3211528 · Cited by 34
Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks
Hong, T., Choi, J.-A., Lim, K., & Kim, P. (2020) · Sensors · doi:10.3390/s21010199 · Cited by 33
Self-citation
Beyond phase-in: assessing impacts on disinformation of the EU Digital Services Act
Nannini, L., Bonel, E., Bassi, D., & Maggini, M. J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00467-w · Cited by 38
Online Advertising Security: Issues, Taxonomy, and Future Directions
Pooranian, Z., Conti, M., Haddadi, H., & Tafazolli, R. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3118271 · Cited by 29
Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique
Rayan, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2500772 · Cited by 29
Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review
Alatrany, A. S., Hussain, A. J., Mustafina, J., & Al‐Jumeily, D. (2022) · IEEE Access · doi:10.1109/access.2022.3182543 · Cited by 26
The Future of Electronic Commerce in the IoT Environment
Lazić, A., Milić, S. D., & Vukmirović, D. (2024) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer19010010 · Cited by 21
Exploring the Role of Artificial Intelligence in Digital Marketing Strategies
Tauheed, J., Shabbir, A., & Pervez, M. S. (2024) · Journal of Business Communication & Technology · doi:10.56632/bct.2024.3105 · Cited by 35
Artificial Intelligence (AI) in Advertising
Argan, M., Dinç, H., Kaya, S., & Argan, M. T. (2023) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.28331 · Cited by 38
Is It Transparent or Surveillant? The Effects of Personalized Advertising and Privacy Policy on Advertising Effectiveness
Lee, S., Moon, W.-K., & Song, Y. G. (2024) · Journal of Interactive Advertising · doi:10.1080/15252019.2024.2417422 · Cited by 26
Space situational awareness systems: Bridging traditional methods and artificial intelligence
Jia, Q., Xiao, J., Bai, L., Zhang, Y., Zhang, R., & Feroskhan, M. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.11.025 · Cited by 23
Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice
Arachchige, J. A., Quach, S., Roca, E., Liu, B., Liew, A. W., & Earl, G. (2022) · Journal of Consumer Behaviour · doi:10.1002/cb.2055 · Cited by 22
Artificial Intelligence and Digital Marketing in Jordan: Opportunities and Challenges
Bataineh, A. Q., & Qasim, D. (2023) · Studies in big data · doi:10.1007/978-3-031-42455-7_26 · Cited by 21
The Change of Digital Marketing with Artificial Intelligence
Efendi̇oğlu, İ. H. (2023) · The Proceedings of the International Conference on Applied Research in Management, Economics and Accounting. · doi:10.33422/7th.iarmea.2023.07.101 · Cited by 25
Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization
Nikolajeva, A., & Teilāns, A. (2021) · European Conference on Modelling and Simulation (ECMS) · doi:10.7148/2021-0125 · Cited by 19
A Deep Learning-Based Hybrid CNN-LSTM Model for Location-Aware Web Service Recommendation
Pandey, A., Mannepalli, P. K., Gupta, M., Dangi, R., & Choudhary, G. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11687-w · Cited by 19
Social media mining in drug development—Fundamentals and use cases
Koss, J., Rheinlaender, A., Truebel, H., & Bohnet-Joschko, S. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.08.012 · Cited by 17
A machine learning based classifier for topological quantum materials
Rasul, A., Hossain, M. S., Dastider, A. G., Roy, H. S., Hasan, M. Z., & Khosru, Q. D. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-68920-8 · Cited by 17
Explainable text-based features in predictive models of crowdfunding campaigns
Pekar, V., Candi, M., Beltagui, A., Stylos, N. D., & Liu, W. (2024) · Annals of Operations Research · doi:10.1007/s10479-023-05800-w · Cited by 14
A User Study on the Acceptance of Native Advertising in Generative IR
Zelch, I., Hagen, M., & Potthast, M. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638316 · Cited by 14
The evolving role of AI and ML in digital promotion: a systematic review and research agenda
Lakshika, V. G. P., Chathuranga, B. T. K., & Jayarathne, P. G. S. A. (2024) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00367-2 · Cited by 13
BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising
Lyu, Q., Li, H., Zhou, R., Zhang, J., Zhao, N., & Liu, Y. (2022) · Security and Communication Networks · doi:10.1155/2022/3043489 · Cited by 12
A digital implantation system for Z-direction yarn of three-dimensional preform based on flexible oriented woven process
Guo, Z., Huang, H., Shan, Z., Huang, J., Hou, Z., & Li, W. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105385 · Cited by 12
Tüketicilerin Üretken Yapay Zekâ Uygulamaları ile Oluşturulan Reklamlara Yönelik Algı ve Davranışları Üzerine Bir Saha Araştırması
Çeber, B., & Çeliker, S. (2024) · Akdeniz Üniversitesi İletişim Fakültesi Dergisi · doi:10.31123/akil.1539417 · Cited by 11
Seeing is Believing: Exploring Deepfake Video Ads and Brand Loyalty in the Experience Economy
Arachchi, H. A. D. M., Samarasinghe, D., & Wickramasinghe, A. (2025) · Journal of Global Marketing · doi:10.1080/08911762.2025.2469293 · Cited by 11
Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks
Kasperek, D., Antonowicz, P., Baranowski, M., Sokołowska, M., & Podpora, M. (2023) · Sensors · doi:10.3390/s23125424 · Cited by 10
Ancillary Services in Targeted Advertising: From Prediction to Prescription
Borenstein, A., Mangal, A., Perakis, G., Poninghaus, S., Singhvi, D., Lami, O. S., & Lua, J. W. (2023) · Manufacturing & Service Operations Management · doi:10.1287/msom.2020.0491 · Cited by 12
Application of artificial intelligence in digital marketing
Marić, M., Grljević, O., & Gluščević, L. (2024) · Anali Ekonomskog fakulteta u Subotici · doi:10.5937/aneksub2300033m · Cited by 12
Classification of Digital Marketing Targeted Data Using Machine Learning Techniques
Sharma, A., Srinivasulu, A., Barua, T., & Tiwari, A. (2021) · IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) · doi:10.1109/tribes52498.2021.9751646 · Cited by 9
Machine learning approach to study quantum phase transitions of a frustrated one dimensional spin-1/2 system
Rahaman, S. S., Haldar, S., & Kumar, M. (2023) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/acb030 · Cited by 9
Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression
Yadav, P., Bhargava, C. P., Gupta, D., Kumari, J., Acharya, A., & Dubey, M. (2024) · International Journal of experimental research and review · doi:10.52756/ijerr.2024.v38.012 · Cited by 9
Online Advertising Dataset Using ANN (Artificial Neural Networks) and LR (Linear Regression Techniques)
Srinivasulu, A., Chowdappa, K. B., Babu, M. D., Reddy, L. V., & Kumar, A. V. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-99-1588-0_7 · Cited by 8
Harnessing AI for Next-Generation Service Marketing
Vetrivel, S. C., Arun, V. P., Saravanan, T. P., & Maheswari, R. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-7122-0.ch015 · Cited by 8
Machine learning models in web applications: A comprehensive review
Mani, K., & Shenoy, A. K. B. (2025) · ICT Express · doi:10.1016/j.icte.2025.09.001 · Cited by 8
Targeted Advertising in Social Media Platforms Using Hybrid Convolutional Learning Method besides Efficient Feature Weights
Jokandan, S. M. E., Bayat, P., & Foumani, M. F. (2022) · Journal of Electrical and Computer Engineering · doi:10.1155/2022/6159650 · Cited by 9
The Impact of Online Behavioral Advertising on Consumer Attitude and Impulse Buying: The Moderating Role of Privacy Concerns
Legros, E., Han, Y., & Park, J. E. (2024) · Asia Marketing Journal · doi:10.53728/2765-6500.1638 · Cited by 12
“The rise of artificial intelligence and machine learning techniques has facilitated the creation of pertinent advertisements for users, drawing upon contextual information or pre-existing user data (Choi and Lim 2020).”
Who Should Lead and What to Say? Exploring AI-Dominant vs. Human-Dominant Content Creation for Informational and Emotional Marketing
Lu, X., Guo, W. R., Wang, Y., & Zhang, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2520997 · Cited by 8
Predicting User Click Behavior on Social Media Ads Using Machine Learning
Kumar, A., Kishan, D., Kandpal, H., Saraswat, H., & Singh, J. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128433 · Cited by 7
Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network
Zhou, S., Du, M., Liu, X., & Shen, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30185 · Cited by 7
Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce
Priatna, W. (2024) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v13i2.3698 · Cited by 7
Assessment of Porosity Defects in Ingot Using Machine Learning Methods during Electro Slag Remelting Process
Zhang, G., Hu, Y., Hou, D., Yang, D., Zhang, Q., Hu, Y., & Liu, X. (2022) · Metals · doi:10.3390/met12060958 · Cited by 7
Consumer Distrust: Non-Transparent AI Decision-Making Processes
Başal, M. (2025) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub710.c3021 · Cited by 6
Click Fraud Detection in Online Advertising: A Comparative Study of Machine Learning Models
Abbas, Z. A., Hilal, Z. M., & Jabbar, H. G. (2025) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.150303 · Cited by 6
Yapay Zekâ Uygulamalarının Tüketicilerin Satın Alma Niyeti Üzerine Etkisi: Büyük Hazine Filmi Üzerine Bir Analiz
Dolu, Z. T., & Marangoz, M. (2023) · Pamukkale Üniversitesi İşletme Araştırmaları Dergisi · doi:10.47097/piar.1271887 · Cited by 6
Enabling Autonomous Digital Marketing: A Machine Learning Approach for Consumer Demand Forecasting
Singhal, K., Singh, J., & Sharma, V. (2024) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/ic2pct60090.2024.10486327 · Cited by 6
La inteligencia artificial en la publicidad: una revisión sistemática de la década 2020-2024
Sanguineti, R. G. B., Córdova, C. D. C., & Baltazar, A. T. (2024) · Journal of the Academy · doi:10.47058/joa11.4 · Cited by 6
REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*
Bulut, B., & Yarar, A. E. (2024) · Beykoz Akademi Dergisi · doi:10.14514/beykozad.1551121 · Cited by 6
Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach
Cardona-Acevedo, S., Agudelo-Ceballos, E., Arango-Botero, D. M., Valencia-Arías, A., Dávila, J. R., García, J. A. J., Goycochea, C. F., & Rojas, E. M. (2025) · F1000Research · doi:10.12688/f1000research.160010.1 · Cited by 7
Risks of Digital Transformation: Review of Machine Learning Algorithms in Credit Card Fraud Detection
Gürsoy, G., & Varol, A. (2021) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec54230.2021.9672354 · Cited by 7
A Machine Learning based Approach to Identify User Interests from Social Data
Tahir, R., & Naeem, M. A. (2022) · International Multi-Topic Conference · doi:10.1109/inmic56986.2022.9972956 · Cited by 6
Digital Marketing Data Classification by Using Machine Learning Algorithms
Abd, N. S., Atiyah, O. S., Ahmed, M. T., & Bakhit, A. H. (2024) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.20.1.23 · Cited by 6
Densenet121-DNN-Based Hybrid Approach for Advertisement Classification and User Identification
(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.0630.13 · Cited by 6
AIvertising
Singh, S., Garg, A., & Walsh, J. C. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9561-5.ch003 · Cited by 5
Innovative Approaches to Market Segmentation Using AI in Emerging Economies
Vetrivel, S. C., Saravanan, T. P., Arun, V. P., & Maheswari, R. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-7122-0.ch017 · Cited by 5
Study of the Berezinskii–Kosterlitz–Thouless transition: an unsupervised machine learning approach
Haldar, S., Rahaman, S. S., & Kumar, M. (2024) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ad5d35 · Cited by 5
Examining Platform Strategy for Influencer Marketing Using Text Mining
Choi, J.-A., & Ku, C. S. (2024) · IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science (BCD) · doi:10.1109/bcd61269.2024.10743091 · Cited by 4
Self-citation
Rethinking targeting strategies for SMEs: How artificial intelligence and audience breadth drive advertising performance
Ham, M., & Park, J. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.108930 · Cited by 6
Click Fraud Detection Using Ensemble Classifier
Purwar, A., Jain, A., Chawla, I., Gupta, I., Raj, M., & Jain, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4860-0_2 · Cited by 4
Artificial Intelligence and Marketing
Pires, P. B., & Santos, J. D. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-8958-1.ch005 · Cited by 4
The Effect of Revisiting Technology Acceptance Model on the Behavioral Targeting Declaration
Zaid, M. I. (2024) · Journal Dimensie Management and Public Sector · doi:10.48173/jdmps.v5i1.260 · Cited by 3
Machine Learning Approaches for Enhancing Query Optimization in Large Databases
Eido, W. M., & Yasin, H. M. (2025) · Engineering and Technology Journal · doi:10.47191/etj/v10i03.39 · Cited by 3
A Regression Model for Analysis of User Engagement on Facebook Fan Page for Scientific Dissemination Content
Velazquez-Solis, P. E., Ibarra-Esquer, J. E., Astorga-Vargas, M. A., Flores-Rios, B. L., Carrillo, M., & García, I. (2023) · Programming and Computer Software · doi:10.1134/s036176882308025x · Cited by 3
Digital Marketing Strategy and Consumer Behavior Analysis Through Deep Q-Network (DQN) Models
Shaikh, I. A. K., Devi, K. N., Deshmukh, P., Rajaram, M., Unni, M. V., & Bhatt, A. (2024) · International Conference on Mobile Networks and Wireless Communications (ICMNWC) · doi:10.1109/icmnwc63764.2024.10872145 · Cited by 3
Unlocking Viewer Insights in Linear Television: A Machine Learning Approach
Carreno, J., Nguyen, K. A., Luo, Z., & Fish, A. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-71333-0_4 · Cited by 3
Dijital Reklamcılıkta Makine Öğrenmesi ve Veri Gizliliği
Demirci, V. G. (2022) · Kent Akademisi · doi:10.35674/kent.1145325 · Cited by 3
Comparison of Naïve Bayes with graph based methods for keyphrase extraction in modern standard Arabic language
Loukam, M. (2022) · International Journal of Speech Technology · doi:10.1007/s10772-022-10009-6 · Cited by 3
Analysis of Machine Learning Classifiers’ Efficacy for Social Media Ads Classification
Sharma, R., Varshney, P., Srivastava, D., & Bharati, K. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895494 · Cited by 2
From precision to perception: Human-in-the-loop evaluation of keyword extraction for internet-scale contextual advertising
Cai, J., Leckner, S., & Björklund, J. (2025) · Information Systems · doi:10.1016/j.is.2025.102665 · Cited by 4
Inteligencia artificial y publicidad digital: discursos persuasivos y ciudadanía en la investigación científica
Delgado, R. B. (2026) · Revista de Ciencias de la Comunicación e Información · doi:10.35742/rcci.2026.31.e351 · Cited by 2
AI-Driven Decision-Making in Programmatic Advertising (PA) for Sustainable Digital Marketing
Gündüzyeli, B. (2026) · NMIMS Management Review · doi:10.1177/09711023261427972 · Cited by 4
Shot boundary detection using multimodal Siamese network
Bouyahi, M., & Yassine, B. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15428-4 · Cited by 2
A Conceptual Model for Click Fraud Detection and Prevention in Online Advertising Using Blockchain
Jigalur, R., & Modi, C. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-3569-7_17 · Cited by 2
Machine Learning Algorithms in Target Marketing Analysis
Wu, J. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/v8zrvc27 · Cited by 2
THE POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE IN ADVERTISING: THEORY AND PRACTICE
Bajak, M., & Spendel, Ł. (2024) · Scientific Papers of Silesian University of Technology Organization and Management Series · doi:10.29119/1641-3466.2024.197.1 · Cited by 2
Programmatic Advertising in E-Commerce
Gündüzyeli, B. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-7041-4.ch006 · Cited by 2
PPFM: An Adaptive and Hierarchical Peer-to-Peer Federated Meta-Learning Framework
Yu, Z., Lu, Y., Angelov, P. P., & Suri, N. (2022) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn57253.2022.00086 · Cited by 2
Machine‐Learning and Deep‐Learning Techniques in Social Sciences
Bhagat, H. V., & Singh, M. (2022) · Wiley eBooks · doi:10.1002/9781119861850.ch23 · Cited by 4
Wie kann überwachtes maschinelles Lernen das digitale Marketing unterstützen?
Nufer, G., & Muth, M. (2025) · Springer eBooks · doi:10.1007/978-3-658-46399-1_73-1 · Cited by 1
The Impact of Artificial Intelligence on Fashion: A Study of Consumer Satisfaction in Portugal
Boechat, A. C., Dias, J. A., & Feital, M. P. S. D. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93838-2_19 · Cited by 1
The Use of Artificial Intelligence in Digital Advertising
Numis, T. M. A. A., Abdulla, M. K. M., Hassan, A., & Assali, I. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-87550-2_123 · Cited by 1
Transfer and Application of Artificial Intelligence Technology in Digital Marketing Strategy of Latvian Companies
Batraga, A., Volkova, T., Šalkovska, J., Kaibe, L., Rūtītis, D., Aksjoņenko, E., & Kontina, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6929-5_30 · Cited by 1
Leveraging AI and Machine Learning for Advancing Marketing Research and Practice
Mudre, C. A., & Juschop, N. (2024) · Researcher Academy Innovation Data Analysis · doi:10.69725/raida.v1i3.169 · Cited by 1
Generative AI and Strategic Communication in Social Impact Campaigns
Mendoza, S. C., Regules, B. E., & Almaraz, I. A. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-3799-8.ch008 · Cited by 1
Effectiveness of generative AI and graphic design in fostering consumer engagement
Shah, M. (2025) · European Economic Letters (EEL) · doi:10.52783/eel.v15i3.3386 · Cited by 1
A Hybrid Deep Learning Approach for Predicting Campaign Success
Ajuluchukwu, M., Coffie, L., & Kim, J. (2025) · IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera65747.2025.11154628 · Cited by 1
Optimizing Ad Campaigns: A Deep Dive into Machine Learning Strategies for Advertising Agencies
Prasad, V. K., Dansana, D., Kumar, D. A., Panda, S. K., & Bhoi, T. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-13009-9_1 · Cited by 1
Evaluating social media user trade-off between free platform use and privacy concerns of AI-powered targeted advertising
Uwayezu, E., Tenney, D., & McAdams, A. (2026) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01518-8 · Cited by 1
Market analytics beyond big data: pretrained AI models for industrial order forecasting in Germany
Muth, M., Engel, J., & Nufer, G. (2026) · Journal of Marketing Analytics · doi:10.1057/s41270-026-00489-9 · Cited by 1
Artificial Intelligence for Diabetes-related Complications: The Eye as a Window to the Systemic Health
Yip, M. Y. T., Wang, Z., Gutiérrez, L., Foo, V. H. X., Lim, J., Lim, G., Gunasekeran, D. V., Wong, T. Y., & Ting, D. S. W. (2022) · Royal Society of Chemistry eBooks · doi:10.1039/9781839165498-00305 · Cited by 1
Nonverbal behavioral patterns predict social rejection elicited aggression
Quarmley, M., Zelinsky, G. J., Athar, S., Yang, Z., Drucker, J. H., Samaras, D., & Jarcho, J. M. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108670 · Cited by 1
Quantitative and Qualitative Approaches of User Engagement on Facebook Fan Page
Velazquez-Solis, P. E., Ibarra-Esquer, J. E., Astorga-Vargas, M. A., Flores-Rios, B. L., Carrillo, M., & García, I. (2024) · Proceedings of the Institute for System Programming of RAS · doi:10.15514/ispras-2024-36(1)-9 · Cited by 1
Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification
Peng, X., Zeng, Y., Chen, Y., & Wang, H. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1384474 · Cited by 1
AI-Driven URL Classification for Targeted Advertising in the Digital Era
Doan, T. C., Linh, B. K., Linh, T. K., Trang, N. Q., & Oanh, N. T. K. (2025) · Springer proceedings in business and economics · doi:10.1007/978-3-031-77363-1_13 · Cited by 1

Authors: Choi, J.-A., & Lim, K.
Affiliations: William Paterson University (US)
Corresponding author: Lim, Kiho
ORCID: Choi, Jin-A
Lim, Kiho
Venue: ICT Express
Publisher: Elsevier BV
Published: 2020-09
Volume: 6
Issue: 3
Pages: 175-180
ISSN: 2405-9595 (ISSN-L)
DOI: 10.1016/j.icte.2020.04.012
OpenAlex: W3033252662
Cited by: 139
References: 31
Citations by year: 2026: 15 · 2025: 27 · 2024: 43 · 2023: 25 · 2022: 19 · 2021: 8 · 2020: 2
FWCI: 12.70
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: ICT Express (publishedVersion, cc-by-nc-nd)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: William Paterson University of New Jersey
Topics: Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords: machine learning methods, audience targeting, user-centric approach, click fraud detection

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Identifying machine learning techniques for classification of target advertising

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks