Memory model for web ad effect based on multimodal features

Abstract

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.

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

“…In this paper, a novel fast 4.5-approximation algorithm was developed for problem P s j , p − batch, K i C max , and we evaluate the algorithm performance via computational experiments. We also provide a simple and fast 2-approximation algorithm for the case that all jobs have the same processing time, ( P s j , p j = p, p − batch, K i C max ), improving upon and generalizing the results in [54][55][56][57]. The approximation ratio of the 2-approximation algorithm in this paper is equal to the presented algorithm in [26], but is now simpler to understand and easier to implement.…”

Impression space model for the evaluation of Internet advertising effectiveness

“…As mentioned above, some of the user's own characteristics have been collected during the experiment, which helps us understand how different users behave. We can identify specific behaviors of users when they are browsing advertisements by dividing them into different user types, which proves that some users' own characteristics will affect the click‐through‐rate . In order to prove this point, we will divide the user types and find out whether different user types will produce different behaviors.…”

“…According to the latest research, the average click‐through rate of advertising has been reduced from 30% to less than 0.5%, making it difficult for the click‐through rate to truly reflect the effectiveness of advertising . For brand advertising, the success of advertising depends not only on whether to buy the product after clicking or reading the advertisement, but also whether it forms a long‐term brand effect, that is, the user keeps a part of the impression after browsing the advertisement, and often takes the initiative when the user browses again, they can pay attention to this part of the content …”