Intelligent systems and consumer neuroscience in the age of computational advertising

Zámečník, Robert

doi:10.2478/mmcks-2024-0020

References

Adams, R. (2004) Intelligent advertising. AI & Soc 18, 68-81. https://doi.org/10.1007/s00146-003-0259-9.
Search in Google Scholar Back to article
Aguilar, J., & Garcia, G. (2017). An adaptive intelligent management system of advertising for social networks: A case study of Facebook. IEEE Transactions on Computational Social Systems, 5(1), 20-32.
Search in Google Scholar Back to article
Alvino, L., Pavone, L., Abhishta, A., & Robben, H. (2020). Picking your brains: Where and how neuroscience tools can enhance marketing research. Frontiers in Neuroscience, 14, 577666. https://doi.org/10.3389/fnins.2020.577666.
Search in Google Scholar Back to article
Anagnostopoulos, A., Broder, A. Z., Gabrilovich, E., Josifovski, V., & Riedel, L. (2011). Web page summarization for just-in-time contextual advertising. ACM Transactions on Intelligent Systems and Technology (TIST), 3(1), 1-32. https://doi.org/10.1145/1321440.1321488.
Search in Google Scholar Back to article
Astolfi, L., Fallani, F. D. V., Cincotti, F., Mattia, D., Bianchi, L., Marciani, M. G., ... & Babiloni, F. (2008). Neural basis for brain responses to TV commercials: a high-resolution EEG study. IEEE Transactions on neural systems and rehabilitation engineering, 16(6), 522-531.
Search in Google Scholar Back to article
Ausin-Azofra, J. M., Bigne, E., Ruiz, C., Marín-Morales, J., Guixeres, J., & Alcañiz, M. (2021). Do you see what i see? Effectiveness of 360-Degree vs. 2D video ads using a neuroscience approach. Frontiers in Psychology, 12, 612717. https://doi.org/10.3389/fpsyg.2021.612717.
Search in Google Scholar Back to article
Bae, S. M., Park, S. C., & Ha, S. H. (2003). Fuzzy web ad selector based on web usage mining. IEEE intelligent Systems, 18(6), 62-69. https://doi.org/10.1109/MIS.2003.1249171.
Search in Google Scholar Back to article
Baker, M. J. (Ed.). (2001). Marketing: critical perspectives on business and management (Vol. 2). Taylor & Francis.
Search in Google Scholar Back to article
Bauer, C. (2014). A framework for conceptualizing context for intelligent systems (CCFIS). Journal of Ambient Intelligence and Smart Environments, 6(4), 403-417. https://doi.org/10.3233/AIS-140269.
Search in Google Scholar Back to article
Bauer, C., & Novotny, A. (2017). A consolidated view of context for intelligent systems. Journal of Ambient Intelligence and Smart Environments, 9(4), 377-393. https://doi.org/10.3233/AIS-170445.
Search in Google Scholar Back to article
Başev, S. E. (2024). The role of artificial intelligence (AI) in the future of the advertising industry : Aplications and examples of AI in advertising. International Journal of Education Technology and Scientific Researches, 9(26), 167-183.
Search in Google Scholar Back to article
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial Intelligence in Advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4), 21582440231210759.
Search in Google Scholar Back to article
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46(3), 363-376. https://doi.org/10.1080/00913367.2017.1339368.
Search in Google Scholar Back to article
Broder, A. (2011). Highly dimensional problems in computational advertising. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I 22 (pp. 5-5). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-23780-5_4.
Search in Google Scholar Back to article
Camerer, C., & Yoon, C. (2015). Introduction to the journal of marketing research special issue on neuroscience and marketing. Journal of Marketing Research, 52(4), 423-426. https://doi.org/10.1509/0022-2437-52.4.4.
Search in Google Scholar Back to article
Casillas, J., & López, F. J. M. (Eds.). (2010). Marketing intelligent systems using soft computing: Managerial and research applications.
Search in Google Scholar Back to article
Chapelle, O., Manavoglu, E., & Rosales, R. (2014). Simple and scalable response prediction for display advertising. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4), 1-34. https://doi.org/10.1145/2532128.
Search in Google Scholar Back to article
Cherubino, P., Martinez-Levy, A. C., Caratu, M., Cartocci, G., Di Flumeri, G., Modica, E., ... & Trettel, A. (2019). Consumer behaviour through the eyes of neurophysiological measures: State-of-the-art and future trends. Computational intelligence and neuroscience, 2019. https://doi.org/10.1155/2019/1976847.
Search in Google Scholar Back to article
Chu, S. C., Yim, M. Y. C., & Mundel, J. (2024). Artificial intelligence, virtual and augmented reality, social media, online reviews, and influencers: a review of how service businesses use promotional devices and future research directions. International Journal of Advertising, 1-31. https://doi.org/10.1080/02650487.2024.2325835.
Search in Google Scholar Back to article
Ciorciari, J., Pfeifer, J., & Gountas, J. (2019). An EEG study on emotional intelligence and advertising message effectiveness. Behavioral Sciences, 9(8), 88. https://doi.org/10.3390/bs9080088.
Search in Google Scholar Back to article
Clark, K. R., Leslie, K. R., Garcia-Garcia, M., & Tullman, M. L. (2018). How advertisers can keep mobile users engaged and reduce video-ad blocking: best practices for video-ad placement and delivery based on consumer neuroscience measures. Journal of Advertising Research, 58(3), 311-325. https://doi.org/10.2501/JAR-2018-036.
Search in Google Scholar Back to article
Dahlen, M., & Rosengren, S. (2016). If advertising won't die, what will it be? Toward a working definition of advertising. Journal of Advertising, 45(3), 334-345. https://doi.org/10.1080/00913367.2016.1172387.
Search in Google Scholar Back to article
Dave, K., & Varma, V. (2014). Computational advertising: Techniques for targeting relevant ads. Foundations and Trends® in Information Retrieval, 8(4–5), 263-418.http://dx.doi.org/10.1561/1500000045.
Search in Google Scholar Back to article
de Balanzó, C., & Sabaté, J. (2007). Neuroscience and advertising: The new frontier of persuasion. Tripods. Extra, ISSN 2339-6415, Vol. 2, No. Extra 2007 (IV International Conference on Communication and Reality), 2007 (Issue dedicated to: The crossroads of communication: limits and transgressions), pp. 909-924.
Search in Google Scholar Back to article
Demirel, Ç., Tokuç, A. A., & Tekin, A. T. (2023). Click prediction boosting via Bayesian hyperparameter optimization-based ensemble learning pipelines. Intelligent Systems with Applications, 17, 200185. https://doi.org/10.1016/j.iswa.2023.200185.
Search in Google Scholar Back to article
Diao, F., & Sundar, S. S. (2004). Orienting response and memory for web advertisements: Exploring effects of pop-up window and animation. Communication research, 31(5), 537-567. https://doi.org/10.1177/0093650204267932.
Search in Google Scholar Back to article
Evans, C., Moore, P., & Thomas, A. (2012, July). An intelligent mobile advertising system (iMAS): Location-based advertising to individuals and business. In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (pp. 959-964). IEEE. https://doi.org/10.1109/CISIS.2012.24.
Search in Google Scholar Back to article
Fortunato, V. C. R., Giraldi, J. D. M. E., & de Oliveira, J. H. C. (2014). A review of studies on neuromarketing: Practical results, techniques, contributions and limitations. Journal of Management Research, 6(2), 201. : http://dx.doi.org/10.5296/jmr.v6i2.5446.
Search in Google Scholar Back to article
Fugate, D.L. (2007), "Neuromarketing: a layman's look at neuroscience and its potential application to marketing practice", Journal of Consumer Marketing, Vol. 24 No. 7, pp. 385-394. https://doi.org/10.1108/07363760710834807.
Search in Google Scholar Back to article
Fulgoni, G. M. (2016). Fraud in digital advertising: A multibillion-dollar black hole: How marketers can minimize losses caused by bogus web traffic. Journal of Advertising Research, 56(2), 122-125.
Search in Google Scholar Back to article
Gala, P., & Gligor, D. (2022). Review of neuroscience in marketing: areas, emotions and tools. International Journal of Business Innovation and Research, 27(1), 76-100. https://doi.org/10.1504/IJBIR.2022.120403.
Search in Google Scholar Back to article
Guixeres, J., Bigné, E., Ausin Azofra, J. M., Alcaniz Raya, M., Colomer Granero, A., Fuentes Hurtado, F., & Naranjo Ornedo, V. (2017). Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Frontiers in psychology, 8, 1808. https://doi.org/10.3389/fpsyg.2017.01808.
Search in Google Scholar Back to article
Gough, D., Thomas, J., & Oliver, S. (2017). An introduction to systematic reviews.
Search in Google Scholar Back to article
Guo, J., Mei, T., Liu, F., & Hua, X. S. (2009, July). AdOn: an intelligent overlay video advertising system. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 628-629).
Search in Google Scholar Back to article
Guo, S., Jin, Z., Sun, F., Li, J., Li, Z., Shi, Y., & Cao, N. (2021, May). Vinci: an intelligent graphic design system for generating advertising posters. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-17). https://doi.org/10.1145/3411764.3445117.
Search in Google Scholar Back to article
Guo, C. (2022). Intelligent Voice System Design for Optimizing E-Business Advertising Rhetoric Based on SVM Algorithm. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/1944275.
Search in Google Scholar Back to article
Ha, S.H. (2004). An Intelligent System for Personalized Advertising on the Internet. In: Bauknecht, K., Bichler, M., Pröll, B. (eds) E-Commerce and Web Technologies. EC-Web 2004. Lecture Notes in Computer Science, vol 3182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30077-9_3.
Search in Google Scholar Back to article
Hamelin, N., El Moujahid, O., & Thaichon, P. (2017). Emotion and advertising effectiveness: A novel facial expression analysis approach. Journal of Retailing and Consumer Services, 36, 103-111. https://doi.org/10.1016/j.jretconser.2017.01.001.
Search in Google Scholar Back to article
Harris, J. M., Ciorciari, J., & Gountas, J. (2018). Consumer neuroscience for marketing researchers. Journal of consumer behaviour, 17(3), 239-252. https://doi.org/10.1002/cb.1710.
Search in Google Scholar Back to article
Helberger, N., Huh, J., Milne, G., Strycharz, J., & Sundaram, H. (2020). Macro and exogenous factors in computational advertising: Key issues and new research directions. Journal of Advertising, 49(4), 377-393. https://doi.org/10.1080/00913367.2020.1811179.
Search in Google Scholar Back to article
Huh, J., & Malthouse, E. C. (2020). Advancing computational advertising: Conceptualization of the field and future directions. Journal of Advertising, 49(4), 367-376. https://doi.org/10.1080/00913367.2020.1795759.
Search in Google Scholar Back to article
Jahanbakhsh, K. (2020). Applying multi-armed bandit algorithms to computational advertising. arXiv preprint arXiv:2011.10919. https://doi.org/10.48550/arXiv.2011.10919.
Search in Google Scholar Back to article
Khalil, G. A., Qutp, M. M., & Nada, M. A. (2023). Intelligent Billboards Targeted Advertising Systems. Journal of Art, Design and Music, 2(2), 5. https://doi.org/10.55554/2785-9649.1020.
Search in Google Scholar Back to article
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267. https://doi.org/10.2501/JAR-2018-035.
Search in Google Scholar Back to article
Kushal Dave & Vasudeva Varma (2014), "Computational Advertising: Techniques for Targeting Relevant Ads", Foundations and Trends® in Information Retrieval: Vol. 8: No. 4-5, pp 263-418. http://dx.doi.org/10.1561/1500000045.
Search in Google Scholar Back to article
Lewinski, P., Fransen, M. L., & Tan, E. S. H. (2014). Predicting advertising effectiveness by facial expressions in response to amusing persuasive stimuli. Journal of Neuroscience, Psychology, and Economics, 7(1), 1–14. https://doi.org/10.1037/npe0000012.
Search in Google Scholar Back to article
Leszczynski, G., Salamon, K., & Zieliński, M. (2022). Acceptance of artificial intelligence in advertising agencies. In Conference: 37th International Business-Information-Management Association Conference Cordoba (Vol. 1).
Search in Google Scholar Back to article
Li, H. (2019). Special section introduction: Artificial intelligence and advertising. Journal of advertising, 48(4), 333-337. https://doi.org/10.1080/00913367.2019.1654947.
Search in Google Scholar Back to article
Lim, W. M. (2018). Demystifying neuromarketing. Journal of business research, 91, 205-220. https://doi.org/10.1016/j.jbusres.2018.05.036.
Search in Google Scholar Back to article
Liu, B. (2007). Web data mining: exploring hyperlinks, contents, and usage data (Vol. 1). Heidelberg: springer. https://doi.org/10.1007/978-3-642-19460-3.
Search in Google Scholar Back to article
Liu, B. (2023). Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning. International Journal of Computer Science and Information Technology, 1(1), 17-23. https://doi.org/10.62051/ijcsit.v1n1.03.
Search in Google Scholar Back to article
Nakagawa, T. (2011). The feasibility study of applied neuroscience for advertising and marketing research (Doctoral dissertation, Massachusetts Institute of Technology). https://dspace.mit.edu/handle/1721.1/65785.
Search in Google Scholar Back to article
Nissenbaum, H., & Daniel, H. (2009). TrackMeNot: Resisting surveillance in web search.
Search in Google Scholar Back to article
O’Connell, B., Walden, S., & Pohlmann, A. (2011). Marketing and neuroscience what drives customer decisions. In American Marketing Association (pp. 1-20). https://hoffmanmarcom.com/ama/white-papers/White%20Paper%20Neuroscience%20what%20drives%20cust%20descisions.pdf.
Search in Google Scholar Back to article
Ohme, R., Reykowska, D., Wiener, D., & Choromanska, A. (2009). Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. Journal of Neuroscience, Psychology, and Economics, 2(1), 21. https://doi.org/10.1037/a0015462.
Search in Google Scholar Back to article
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1CAIS.03743.
Search in Google Scholar Back to article
Page, G., & Raymond, J. (2006). Cognitive Neuroscience, Marketing and Research. In ESOMAR Congress, London. https://arf-research-dox.s3.amazonaws.com/Supplementary+Materials/Affectiva/cognitive-neuroscience-marketing-and-research.pdf.
Search in Google Scholar Back to article
Pak, B. K., Mocan, B., Yoldaş, S. Y., & Baz, N. (2018, September). Development of Autonomous Intelligent System for Google Ads. In 2018 Thirteenth International Conference on Digital Information Management (ICDIM) (pp. 102-107). IEEE. https://doi.org/10.1109/ICDIM.2018.8847128.
Search in Google Scholar Back to article
Pham, P., & Wang, J. (2019). Attentivevideo: A multimodal approach to quantify emotional responses to mobile advertisements. ACM Transactions on Interactive Intelligent Systems (TiiS), 9(2-3), 1-30. https://doi.org/10.1145/3232233.
Search in Google Scholar Back to article
Piwowarski, M., Shankar Singh, U., & Nermend, K. (2019). The cognitive neuroscience methods in the analysis of the impact of advertisements in shaping people's health habits. https://www.um.edu.mt/library/oar/handle/123456789/49797
Search in Google Scholar Back to article
Poh, H. L., Yao, J., & Jašic, T. (1998). Neural networks for the analysis and forecasting of advertising and promotion impact. Intelligent Systems in Accounting, Finance & Management, 7(4), 253-268. https://doi.org/10.1002/(SICI)1099-1174(199812)7:4<253::AID-ISAF150>3.0.CO;2-X.
Search in Google Scholar Back to article
Precourt, G. (2015). How Does Neuroscience Work in Advertising?. Journal of Advertising Research, 55(2), 112-113. https://doi.org/10.2501/JAR-55-2-112-113.
Search in Google Scholar Back to article
Qin, H., & Lei, J. (2019, September). The application of virtual reality technology in advertising communication. In 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) (pp. 73-76). IEEE. https://doi.org/10.1109/ICVRIS.2019.00027.
Search in Google Scholar Back to article
Ramsøy, T. Z. (2019). Building a foundation for neuromarketing and consumer neuroscience research: How researchers can apply academic rigor to the neuroscientific study of advertising effects. Journal of Advertising Research, 59(3), 281-294. https://doi.org/10.2501/JAR-2019-034.
Search in Google Scholar Back to article
Sánchez-Fernández, J., Casado-Aranda, L. A., & Bastidas-Manzano, A. B. (2021). Consumer neuroscience techniques in advertising research: A bibliometric citation analysis. Sustainability, 13(3), 1589. https://doi.org/10.3390/su13031589.
Search in Google Scholar Back to article
Sestino, A., & De Mauro, A. (2022). Leveraging artificial intelligence in business: Implications, applications and methods. Technology Analysis & Strategic Management, 34(1), 16-29. https://doi.org/10.1080/09537325.2021.1883583.
Search in Google Scholar Back to article
Silberstein, R. B., & Nield, G. E. (2008). Brain activity correlates of consumer brand choice shift associated with television advertising. International Journal of Advertising, 27(3), 359-380. https://doi.org/10.2501/S0265048708080025.
Search in Google Scholar Back to article
Smidts, A., Hsu, M., Sanfey, A. G., Boksem, M. A., Ebstein, R. B., Huettel, S. A., ... & Yoon, C. (2014). Advancing consumer neuroscience. Marketing Letters, 25, 257-267. https://doi.org/10.1007/s11002-014-9306-1.
Search in Google Scholar Back to article
Solnais, C., Andreu-Perez, J., Sánchez-Fernández, J., & Andréu-Abela, J. (2013). The contribution of neuroscience to consumer research: A conceptual framework and empirical review. Journal of economic psychology, 36, 68-81. https://doi.org/10.1016/j.joep.2013.02.011.
Search in Google Scholar Back to article
Stevens, N. T. (2021). Modern Design of Experiments for Computational Advertising. https://www.birs.ca/workshops/2021/21w5508/files/Nathaniel%20Stevens/Modern_DOE_BIRS2021.pdf.
Search in Google Scholar Back to article
Sun, D. (2022). Application of Traditional Culture in Intelligent Advertising Design System in the Internet Era. Scientific Programming, 2022. https://doi.org/10.1155/2022/7596991.
Search in Google Scholar Back to article
Sung, B., Wilson, N. J., Yun, J. H., & Lee, E. J. (2020). What can neuroscience offer marketing research?. Asia Pacific Journal of Marketing and Logistics, 32(5), 1089-1111. https://doi.org/10.1108/APJML-04-2019-0227.
Search in Google Scholar Back to article
Vakratsas, D., & Ambler, T. (1999). How advertising works: what do we really know?. Journal of marketing, 63(1), 26-43. https://doi.org/10.1177/0022242999063001.
Search in Google Scholar Back to article
Valitutti, A., Strapparava, C., & Stock, O. (2008, March). Textual Affect Sensing for Computational Advertising. In AAAI Spring Symposium: Creative Intelligent Systems (pp. 117-122). https://cdn.aaai.org/Symposia/Spring/2008/SS-08-03/SS08-03-018.pdf.
Search in Google Scholar Back to article
Van Noort, G., Himelboim, I., Martin, J., & Collinger, T. (2020). Introducing a model of automated brand-generated content in an era of computational advertising. Journal of Advertising, 49(4), 411-427. https://doi.org/10.1080/00913367.2020.1795954.
Search in Google Scholar Back to article
Varan, D., Lang, A., Barwise, P., Weber, R., & Bellman, S. (2015). How Reliable Are Neuromarketers' Measures of Advertising Effectiveness?: Data from Ongoing Research Holds No Common Truth among Vendors. Journal of advertising research, 55(2), 176-191. https://doi.org/10.2501/JAR-55-2-176-191.
Search in Google Scholar Back to article
Venkatraman, V., Clithero, J. A., Fitzsimons, G. J., & Huettel, S. A. (2012). New scanner data for brand marketers: How neuroscience can help better understand differences in brand preferences. Journal of consumer psychology, 22(1), 143-153. https://doi.org/10.1016/j.jcps.2011.11.008.
Search in Google Scholar Back to article
Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., ... & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436-452.
Search in Google Scholar Back to article
Wang, Y. J., & Minor, M. S. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychology & Marketing, 25(2), 197-232. https://doi.org/10.1002/mar.20206.
Search in Google Scholar Back to article
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii. https://www.jstor.org/stable/4132319.
Search in Google Scholar Back to article
Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37. https://doi.org/10.62051/ijcsit.v1n1.05.
Search in Google Scholar Back to article
Yang, Y., Yang, Y. C., Jansen, B. J., & Lalmas, M. (2017). Computational advertising: A paradigm shift for advertising and marketing?. IEEE Intelligent Systems, 32(3), 3-6. https://doi.org/10.1109/MIS.2017.58.
Search in Google Scholar Back to article
Yang, X., Sun, D., Zhu, R., Deng, T., Guo, Z., Ding, Z., ... & Zhu, Y. (2019a). Aiads: Automated and intelligent advertising system for sponsored search. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1881-1890). https://doi.org/10.1145/3292500.3330782.
Search in Google Scholar Back to article
Yang, Y., Jansen, B. J., Yang, Y., Guo, X., & Zeng, D. (2019b). Keyword optimization in sponsored search advertising: A multilevel computational framework. IEEE Intelligent Systems, 34(1), 32-42. https://doi.org/10.1109/MIS.2019.2893590.
Search in Google Scholar Back to article
Yun, J. T., Segijn, C. M., Pearson, S., Malthouse, E. C., Konstan, J. A., & Shankar, V. (2020). Challenges and future directions of computational advertising measurement systems. Journal of Advertising, 49(4), 446-458. https://doi.org/10.1080/00913367.2020.1795757.
Search in Google Scholar Back to article
Zuboff, S. (2023). The age of surveillance capitalism. In Social theory re-wired (pp. 203-213). Routledge.
Search in Google Scholar Back to article

Intelligent systems and consumer neuroscience in the age of computational advertising

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