多重因素下基于多模态特征的网页广告效果

虽然互联网广告效果的相关研究已取得较好成果，但仍缺乏对网页中各条目与广告间相互作用的深入研究，也缺乏不同因素作用下用户行为和广告效果的透彻分析，广告衡量标准也存在不足。因此，提出一种基于多模态特征融合的方法针对多重因素作用下的互联网广告效果与用户行为模式进行研究。通过对多模态特征进行定量分析，验证广告的吸引力效应，总结不同条件下的注意力效应；针对用户行为信息进行频繁模式挖掘，并结合数据特点提出DFBP算法定向挖掘用户最常见的浏览模式；提出将记忆力作为衡量广告质量的一项新指标，利用频繁模式改进Random Forest算法，融合多模态特征构建广告记忆力模型。实验结果表明，所构建的记忆力模型不仅准确率高达91.64%，且具有良好的鲁棒性。

Effect of Web advertisement based on multi-modal features under the influence of multiple factors

Although the relevant research on Web advertisement effect has achieved good results, there are still a lack of thorough research on the interaction between advertisement and each blue link in a Web page, as well as a lack of thorough analysis of the impact of user characteristics and advertising features, and advertising metrics are also inappropriate. Therefore, a method based on multi-modal feature fusion was proposed to study the effectiveness of Internet advertising and user behavior patterns under the influence of multiple factors. Through the quantitative analysis of multi-modal features, the attractiveness of advertising was verified, and the attention effects under different conditions were summarized. By mining frequent patterns of user behavior information and combining with the characteristics of the data, the Directional Frequent Browsing Patterns (DFBP) algorithm was proposed to directionally mine the most common browsing patterns of users with fixed-length. Memory was used as a new index to measure the quality of advertising, and the random forest algorithm was improved by frequent pattern, then a new advertising memory model was built by fusing multimodal features. Experimental results show that the memory model has an accuracy of 91.64%, and it has good robustness.