Prediction of instantaneous likeability of advertisements using deep learning

[1]  Vicki G. Morwitz,et al.  When Do Purchase Intentions Predict Sales? , 2006 .

[2]  K. Pearson.  VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.

[3]  Dimitrios Hatzinakos,et al.  CNN-based Prediction of Frame-Level Shot Importance for Video Summarization , 2017, 2017 International Conference on New Trends in Computing Sciences (ICTCS).

[4]  Jong-Seok Lee,et al.  Music Popularity: Metrics, Characteristics, and Audio-Based Prediction , 2018, IEEE Transactions on Multimedia.

[5]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[6]  William H. Hampton,et al.  Predicting Advertising success beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling , 2015 .

[7]  Daniel Gatica-Perez,et al.  The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs , 2013, IEEE Transactions on Multimedia.

[8]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[9]  Przemysław Rokita,et al.  Predicting Popularity of Online Videos Using Support Vector Regression , 2017, IEEE Transactions on Multimedia.

[10]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[11]  Annie Lang,et al.  The effects of production pacing and arousing content on the information processing of television messages , 1999 .

[12]  Gerald C. Stone,et al.  Recall, Liking, and Creativity in TV Commercials: A New Approach , 2000, Journal of Advertising Research.

[13]  J. Bartko.  The Intraclass Correlation Coefficient as a Measure of Reliability , 1966, Psychological reports.

[14]  G. Mandler.  Recognizing: The judgment of previous occurrence. , 1980 .

[15]  Yale Song,et al.  TVSum: Summarizing web videos using titles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  D. Ariely,et al.  Neuromarketing: the hope and hype of neuroimaging in business , 2010, Nature Reviews Neuroscience.

[17]  Laura Chamberlain,et al.  What is "neuromarketing"? A discussion and agenda for future research. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[18]  Dimitrios Hatzinakos,et al.  Estimation of affective dimensions using CNN-based features of audiovisual data , 2019, Pattern Recognit. Lett..

[19]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[20]  Zhong Ming,et al.  Improved softmax loss for deep learning-based face and expression recognition , 2019, Cogn. Comput. Syst..

[21]  Daniel McDuff,et al.  Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads , 2014, IEEE Transactions on Affective Computing.

[22]  Dimitrios Hatzinakos,et al.  Statistical Selection of CNN-based Audiovisual Features for Instantaneous Estimation of Human Emotional States , 2017, 2017 International Conference on New Trends in Computing Sciences (ICTCS).

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Norimichi Tsumura,et al.  Advertisement Effectiveness Estimation Based on Crowdsourced Multimodal Affective Responses , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[25]  Kim-Shyan Fam.  What Drives Ad Likeability/dislikeability in Hong Kong and Thailand? , 2006 .

[26]  David Walker,et al.  Why liking matters , 1994 .

[27]  Michael S. Minor,et al.  Validity, reliability, and applicability of psychophysiological techniques in marketing research , 2008 .

[28]  R. Pieters,et al.  Emotion-Induced Engagement in Internet Video Advertisements , 2012 .

[29]  Heng Tao Shen,et al.  Video Captioning With Attention-Based LSTM and Semantic Consistency , 2017, IEEE Transactions on Multimedia.

[30]  Russell I. Haley,et al.  The ARF Copy Research Validity Project , 2000, Journal of Advertising Research.

[31]  Xingquan Zhu,et al.  Deep Learning for User Interest and Response Prediction in Online Display Advertising , 2020, Data Science and Engineering.

[32]  Shinobu Kitayama,et al.  Advancing consumer neuroscience , 2014 .

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  P. Neijens,et al.  Effects of Advertising Likeability: A 10-Year Perspective , 2006, Journal of Advertising Research.

[35]  E. D. Plessis,et al.  Recognition versus recall , 1994 .

[36]  George Trigeorgis,et al.  End-to-End Multimodal Emotion Recognition Using Deep Neural Networks , 2017, IEEE Journal of Selected Topics in Signal Processing.

[37]  Stefanos D. Kollias,et al.  An Optimized Key-Frames Extraction Scheme Based on SVD and Correlation Minimization , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[38]  Spyros Makridakis,et al.  Accuracy measures: theoretical and practical concerns☆ , 1993 .

[39]  Rana El Kaliouby,et al.  Automatic measurement of ad preferences from facial responses gathered over the Internet , 2014, Image Vis. Comput..

[40]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[41]  David S. Waller,et al.  Identifying likeable attributes: a qualitative study of television advertisements in Asia , 2006 .

[42]  J. Payne,et al.  An overall probability of winning heuristic for complex risky decisions: Choice and eye fixation evidence , 2014 .

[43]  William M. Campbell,et al.  Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction , 2016, AVEC@ACM Multimedia.

[44]  Scott A. Huettel,et al.  New Scanner Data for Brand Marketers: How Neuroscience Can Help Better Understand Differences in Brand Preferences , 2011 .