A Smart Ad Display System

Published Online:https://doi.org/10.1287/isre.2020.0128

References

  • Abdi H (2007) The Bonferroni and Sidak corrections for multiple comparisons. Salkind N, ed. Encyclopedia of Measurement and Statistics, vol. 3 (Sage, Thousand Oaks, CA), 103–107.Google Scholar
  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6):734–749.CrossrefGoogle Scholar
  • Adomavicius G, Bauman K, Tuzhilin A, Unger M (2022) Context-aware recommender systems: From foundations to recent developments. Ricci F, Rokach L, Shapira B, eds. Recommender Systems Handbook (Springer US, New York), 211–250.CrossrefGoogle Scholar
  • Bauer C, Kryvinska N, Strauss C (2016) The business with digital signage for advertising. Ricciardi F, Harfouche A, eds. Information and Communication Technologies in Organizations and Society. Lecture Notes in Information Systems and Organisation, vol. 15 (Springer, Cham, Switzerland), 285–302.CrossrefGoogle Scholar
  • Bauman K, Tuzhilin A (2022) Know thy context: Parsing contextual information from user reviews for recommendation purposes. Inform. Systems Res. 33(1):179–202.LinkGoogle Scholar
  • Bi X, Yang M, Adomavicius G (2023) Consumer acquisition for recommender systems: A theoretical framework and empirical evaluations. Inform. Systems Res., ePub ahead of print May 15, https://doi.org/10.1287/isre.2023.1229.Google Scholar
  • Brasel SA, Gips J (2008) Breaking through fast-forwarding: Brand information and visual attention. J. Marketing 72(6):31–48.CrossrefGoogle Scholar
  • Cameron AF, Webster J (2013) Multicommunicating: Juggling multiple conversations in the workplace. Inform. Systems Res. 24(2):352–371.LinkGoogle Scholar
  • Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. Eur. Conf. Comput. Vision (Springer International Publishing, Cham, Switzerland), 213–229.Google Scholar
  • Chandy RK, Tellis GJ, MacInnis DJ, Thaivanich P (2001) What to say when: Advertising appeals in evolving markets. J. Marketing Res. 38(4):399–414.CrossrefGoogle Scholar
  • Choi H, Mela CF, Balseiro SR, Leary A (2020) Online display advertising markets: A literature review and future directions. Inform. Systems Res. 31(2):556–575.LinkGoogle Scholar
  • Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (Lawrence Erlbaum, Mahwah, NJ).Google Scholar
  • Dara S, Chowdary C, Kumar C (2020) A survey on group recommender systems. J. Intelligent Inform. Systems 54(2):271–295.CrossrefGoogle Scholar
  • Dean S (2020) Forget credit cards—Now you can pay with your face. Creepy or cool? Los Angeles Times (August 14), https://www.latimes.com/business/technology/story/2020-08-14/facial-recognition-payment-technology.Google Scholar
  • Deldjoo Y, Ferrari Dacrema M, Constantin MG, Eghbal-Zadeh H, Cereda S, Schedl M, Ionescu B, Cremonesi P (2019) Movie genome: Alleviating new item cold start in movie recommendation. User Model. User-Adapted Interaction 29:291–343.CrossrefGoogle Scholar
  • Deng Y, Mela CF (2018) TV viewing and advertising targeting. J. Marketing Res. 55(1):99–118.CrossrefGoogle Scholar
  • Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. Preprint, submitted October 22, https://arxiv.org/abs/2010.11929.Google Scholar
  • Ekman P (1999) Basic emotions. Dalgleish T, Power M, eds. Handbook of Cognition and Emotion (Wiley, Hoboken, NJ), 45–60.CrossrefGoogle Scholar
  • Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J. Personality Soc. Psych. 17(2):124–129.CrossrefGoogle Scholar
  • Fasel B, Luettin J (2003) Automatic facial expression analysis: A survey. Pattern Recognition 36(1):259–275.CrossrefGoogle Scholar
  • Gao J, Rong Y, Tian X, Yao Y (2023) Improving convenience or saving face? An empirical analysis of the use of facial recognition payment technology in retail. Inform. Systems Res., ePub ahead of print February 17, https://doi.org/10.1287/isre.2023.1205.Google Scholar
  • Gayle D (2012) The ‘creepy’ mannequin that spies on you: Shops use dummies fitted with airport security to profile customers. Daily Mail (November 22), https://www.dailymail.co.uk/sciencetech/article-2235848/The-creepy-mannequin-stares-Fashion-retailers-adapt-airport-security-technology-profile-customers.html.Google Scholar
  • Goldsmith RE, Lafferty BA, Newell SJ (2000) The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. J. Advertising 29(3):43–54.CrossrefGoogle Scholar
  • Grand View Research (2023) Digital Signage Market Size, Share, & Trend Analysis Report by Type, by Component, by Technology, by Application, by Location, by Content Category, by Size, by Region, and Segment Forecasts, 2023–2030. Report (Grand View Research, San Francisco).Google Scholar
  • Guitart IA, Stremersch S (2021) The impact of informational and emotional television ad content on online search and sales. J. Marketing Res. 58(2):299–320.CrossrefGoogle Scholar
  • Han SP, Shavitt S (1994) Persuasion and culture: Advertising appeals in individualistic and collectivistic societies. J. Experiment. Soc. Psych. 30(4):326–350.CrossrefGoogle Scholar
  • Hansen DW, Ji Q (2010) In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Machine Intelligence 32(3):478–500.CrossrefGoogle Scholar
  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 770–778.Google Scholar
  • Hudgins C (2019) 7 elements of print advertising. Chron.com (February 4), https://smallbusiness.chron.com/7-elements-print-advertising-15325.html.Google Scholar
  • Ji Q, Yang X (2002) Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging 8(5):357–377.CrossrefGoogle Scholar
  • Li S, Deng W (2016) Real world expression recognition: A highly imbalanced detection problem. 2016 Int. Conf. Biometrics (ICB) (IEEE, Piscataway, NJ), 1–6.Google Scholar
  • Lu S, Xiao L, Ding M (2016) A video-based automated recommender (VAR) system for garments. Marketing Sci. 35(3):484–510.LinkGoogle Scholar
  • Lu S, Kim H, Zhou Y, Xiao L, Ding M (2022) Audio and visual analytics in marketing and artificial empathy. Foundations Trends Marketing 16(4):422–493.Google Scholar
  • MacInnis DJ, Rao AG, Weiss AM (2002) Assessing when increased media weight of real-world advertisements helps sales. J. Marketing Res. 39(4):391–407.CrossrefGoogle Scholar
  • McQuarrie EF, Mick DG (1999) Visual rhetoric in advertising: Text-interpretive, experimental, and reader-response analyses. J. Consumer Res. 26(1):37–54.CrossrefGoogle Scholar
  • Miniard PW, Bhatla S, Lord KR, Dickson PR, Unnava HR (1991) Picture-based persuasion processes and the moderating role of involvement. J. Consumer Res. 18(1):92–107.CrossrefGoogle Scholar
  • Misra I, Girdhar R, Joulin A (2021) An end-to-end transformer model for 3D object detection. Proc. IEEE/CVF Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 2906–2917.Google Scholar
  • Muhammad K, Wang Q, O’Reilly-Morgan D, Tragos E, Smyth B, Hurley N, Geraci J, Lawlor A (2020) Fedfast: Going beyond average for faster training of federated recommender systems. Proc. 26th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1234–1242.Google Scholar
  • Netzer O, Toubia O, Bradlow ET, Dahan E, Evgeniou T, Feinberg FM, Feit EM, et al. (2008) Beyond conjoint analysis: Advances in preference measurement. Marketing Lett. 19(3–4):337–354.CrossrefGoogle Scholar
  • O’Brien M (2015) Why are ads with animals so appealing? ClickZ (December 24), https://www.clickz.com/why-are-ads-with-animals-so-popular.Google Scholar
  • Olenski S (2016) Can in-store video help or hurt the customer experience? Forbes (December 9), https://www.forbes.com/sites/steveolenski/2016/12/09/can-in-store-video-help-or-hurt-the-customer-experience/?sh=2da8e32f65d6.Google Scholar
  • Pan S (2011) The role of TV commercial visuals in forming memorable and impressive destination images. J. Travel Res. 50(2):171–185.CrossrefGoogle Scholar
  • Ramanathan S, McGill AL (2007) Consuming with others: Social influences on moment-to-moment and retrospective evaluations of an experience. J. Consumer Res. 34(4):506–524.CrossrefGoogle Scholar
  • Ramstad E (2012) Big brother, now at the mall. Facial-ID software recognizes age, sex—For the sake of a sales pitch. Wall Street Journal (October 8), https://www.wsj.com/articles/SB10000872396390444897304578044322254166986.Google Scholar
  • Ricci F, Rokach L, Shapira B, eds. (2022) Recommender Systems Handbook, 3rd ed. (Springer, New York).CrossrefGoogle Scholar
  • Roberts A (2012) In some stores, the mannequins are watching you. Bloomberg (December 6), https://www.bloomberg.com/news/articles/2012-12-06/in-some-stores-the-mannequins-are-watching-you.Google Scholar
  • Rosbergen E, Pieters R, Wedel M (1997) Visual attention to advertising: A segment-level analysis. J. Consumer Res. 24(3):305–314.CrossrefGoogle Scholar
  • Schaeffler J (2012) Digital Signage: Software, Networks, Advertising, and Displays: A Primer for Understanding the Business (Routledge, New York).CrossrefGoogle Scholar
  • Springwise (2012) At London bus stop, interactive ad shows different content to men and women. https://www.springwise.com/london-bus-stop-interactive-ad-shows-content-men-women.Google Scholar
  • Teixeira T, Picard R, Kaliouby RE (2014) Why, when, and how much to entertain consumers in advertisements? A web-based facial tracking field study. Marketing Sci. 33(6):809–827.LinkGoogle Scholar
  • Teixeira T, Wedel M, Pieters R (2012) Emotion-induced engagement in Internet video advertisements. J. Marketing Res. 49(2):144–159.CrossrefGoogle Scholar
  • Tellis GJ, MacInnis DJ, Tirunillai S, Zhang Y (2019) What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. J. Marketing 83(4):1–20.CrossrefGoogle Scholar
  • Urban G, Hauser J (1993) Design and Marketing of New Products, 2nd ed. (Prentice Hall, Hoboken, NJ).Google Scholar
  • Van Leeuwen B, Noussair CN, Offerman T, Suetens S, Van Veelen M, Van De Ven J (2017) Predictably angry-facial cues provide a credible signal of destructive behavior. Management Sci. 64(7):3352–3364.LinkGoogle Scholar
  • Venkatraman V, Dimoka A, Pavlou PA, Vo K, Hampton W, Bollinger B, Hershfield HE, Ishihara M, Winer RS (2015) Predicting advertising access beyond traditional measures: New insights from neurophysiological methods and market response modeling. J. Marketing Res. 52(4):436–452.CrossrefGoogle Scholar
  • Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition 48(9):2839–2846.CrossrefGoogle Scholar
  • Xiao L, Ding M (2014) Just the faces: Exploring the effects of facial features in print advertising. Marketing Sci. 33(3):338–352.LinkGoogle Scholar
  • Xiao L, Kim H, Ding M (2013) An introduction to audio and visual research and applications in marketing. Malhotra NK, ed. Review of Marketing Research, vol. 10 (Emerald Publishing Limited, Leeds, UK), 213–253.CrossrefGoogle Scholar
  • Yi C, Jiang Z, Li X, Lu X (2019) Leveraging user-generated content for product promotion: The effects of firm-highlighted reviews. Inform. Systems Res. 30(3):711–725.LinkGoogle Scholar
  • Yim MY, Yoo SC, Till BD, Eastin MS (2010) In-store video advertising effectiveness: Three new studies provide in-market field data. J. Advertising Res. 50(4):386–402.CrossrefGoogle Scholar
  • Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? Preprint, submitted November 6, https://arxiv.org/abs/1411.1792.Google Scholar
  • Yue F (2019) AI blends with beauty products for personalized skincare, products like smarter toothbrush. USA Today (August 20), https://www.usatoday.com/story/tech/2019/08/19/ai-ar-helps-create-personalized-beauty-products/1799522001/.Google Scholar
  • Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Lett. 23(10):1499–1503.CrossrefGoogle Scholar
  • Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput. Surveys 35(4):399–458.CrossrefGoogle Scholar
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