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Memory Model for Web Ad Effect Based on Multimodal Features

机译：基于多峰特征的Web广告效果记忆模型

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By studying the distribution of the user's attention on the webpage, we find that the user's attention is indeed influenced by many factors, such as the user's cognitive style and advertisement layout, which verifies the attractiveness effect of the advertisement. Moreover, we innova-tively propose the concept of enhancement or inhibitory effects to the ads and the ideas of cognitive identity and cognitive diversity. In addition, we explored the sequences of user behaviors and mine the association rules of the click behavior in ROIs by using the Apriori algorithm in various cognitive styles and different ad layouts. Then we proposed the DFBP algorithm to find the most common browsing patterns of users. Finally, we proposed the memory model to measure the ad's effectiveness. Through the multimodal feature integration approach, we build an the advertising memory model based on the Hierarchical Random Forest structure and predict the user's memory of the advertisements. Our innovative approach compensates for the shortcomings of the CTR and the conversion rate with the eye-movement features and the cognitive styles.
机译：通过研究用户注意力在网页上的分布，我们发现用户的注意力确实受到许多因素的影响，例如用户的认知风格和广告布局，从而验证了广告的吸引力。此外，我们创新性地提出了增强或抑制广告效果的概念以及认知身份和认知多样性的概念。此外，我们使用各种认知风格和不同广告布局中的Apriori算法，探索了用户行为的序列，并挖掘了ROI中点击行为的关联规则。然后，我们提出了DFBP算法来查找用户最常见的浏览模式。最后，我们提出了记忆模型来衡量广告的有效性。通过多模式特征集成方法，我们建立了基于分层随机森林结构的广告记忆模型，并预测了用户对广告的记忆。我们的创新方法通过眼动功能和认知方式弥补了点击率和转化率的不足。

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