Memory model for web ad effect based on multimodal features

Web ad effect evaluation is a challenging problem in web marketing research. Although the analysis of web ad effectiveness has achieved excellent results, there are still some deficiencies. First, there is a lack of an in‐depth study of the relevance between advertisements and web content. Second, there is not a thorough analysis of the impacts of users and advertising features on user browsing behaviors. And last, the evaluation index of the web advertisement effect is not adequate. Given the above problems, we conducted our work by studying the observer's behavioral pattern based on multimodal features. First, we analyze the correlation between ads and links with different searching results and further assess the influence of relevance on the observer's attention to web ads using eye‐movement features. Then we investigate the user's behavioral sequence and propose the directional frequent‐browsing pattern algorithm for mining the user's most commonly used browsing patterns. Finally, we offer the novel use of “memory” as a new measure of advertising effectiveness and further build an advertising memory model with integrated multimodal features for predicting the efficacy of web ads. A large number of experiments have proved the superiority of our method.

[1]  Meng Wang,et al.  Does Vertical Bring more Satisfaction?: Predicting Search Satisfaction in a Heterogeneous Environment , 2015, CIKM.

[2]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[3]  D. Kellogg,et al.  Visual observations of live zooplankters: evasion, escape, and chemical defenses , 1980 .

[4]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  Liu Yiqun,et al.  Sponsored Search Performance Analysis Based on User Behavior Information , 2011 .

[7]  S. Viswanathan,et al.  Quality Uncertainty and the Performance of Online Sponsored Search Markets: An Empirical Investigation , 2007 .

[8]  Chengjie Sun,et al.  Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization , 2016, Electron. Commer. Res. Appl..

[9]  Qi Zhao,et al.  Webpage Saliency , 2014, ECCV.

[10]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[11]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[12]  X. Drèze,et al.  Internet advertising: Is anybody watching? , 2003 .

[13]  Michel Wedel,et al.  Eye Fixations on Advertisements and Memory for Brands: A Model and Findings , 2000 .

[14]  Berthier A. Ribeiro-Neto,et al.  Impedance coupling in content-targeted advertising , 2005, SIGIR '05.

[15]  Hong Wang,et al.  Shared-nearest-neighbor-based clustering by fast search and find of density peaks , 2018, Inf. Sci..

[16]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[17]  Yiqun Liu,et al.  Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information , 2015, SIGIR.

[18]  Eva A van Reijmersdal,et al.  Mixing advertising and editorial content in radio programmes , 2011 .

[19]  Tim K Marks,et al.  SUN: A Bayesian framework for saliency using natural statistics. , 2008, Journal of vision.

[20]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

[21]  Peter Harrington,et al.  Machine Learning in Action , 2012 .

[22]  Vandana Ramachandran,et al.  An empirical investigation of the performance of online sponsored search markets , 2007, ICEC.

[23]  Dai Jin-bo,et al.  Extracting Attention Information Algorithm Based on Contrast Sensitivity and Markov Chain , 2010 .

[24]  Eva A. van Reijmersdal,et al.  Mixing advertising and editorial content in radio programmes: appreciation and recall of brand placements versus commercials , 2011 .