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Memory model for web ad effect based on multimodal features
Author(s) -
Wang Hong,
Song YongQiang,
Wang LuTong,
Hu XiaoHong
Publication year - 2020
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24214
Subject(s) - computer science , relevance (law) , web navigation , web analytics , web page , information retrieval , world wide web , web mining , online advertising , web modeling , the internet , web intelligence , political science , law
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.