计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 987-994.DOI: 10.11772/j.issn.1001-9081.2017102425

• 数据科学与技术 • 上一篇    下一篇

多重因素下基于多模态特征的网页广告效果

胡晓红1,2,3, 王红1,2,3, 任衍具4, 周莹1,2,3   

  1. 1. 山东师范大学 信息科学与工程学院, 济南 250358;
    2. 山东省分布式计算软件新技术重点实验室, 济南 250014;
    3. 山东师范大学 生命科学研究院, 济南 250358;
    4. 山东师范大学 心理学院, 济南 250358
  • 收稿日期:2017-10-12 修回日期:2017-11-15 出版日期:2018-04-10 发布日期:2018-04-09
  • 通讯作者: 王红
  • 作者简介:胡晓红(1993-),女,山东枣庄人,硕士研究生,CCF会员,主要研究方向:机器学习、数据挖掘、眼动追踪、在线广告;王红(1966-),女,天津人,教授,博士,CCF高级会员,主要研究方向:移动社会软件、复杂网络、工作流;任衍具(1977-),男,山东济宁人,副教授,博士,主要研究方向:视知觉、视觉注意、工作记忆;周莹(1993-),女,山东潍坊人,硕士研究生,CCF会员,主要研究方向:眼动追踪、机器学习、数据挖掘。
  • 基金资助:
    国家自然科学基金资助项目(61672329);山东省科技计划项目(2014GGX101026);山东省教育科学规划项目(ZK1437B010);山东师范大学研究生科研创新基金资助项目(SCX201747)。

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

HU Xiaohong1,2,3, WANG Hong1,2,3, REN Yanju4, ZHOU Ying1,2,3   

  1. 1. School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250358, China;
    2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan Shandong 250014, China;
    3. Institute of Life Sciences, Shandong Normal University, Jinan Shandong 250358, China;
    4. School of Psychology, Shandong Normal University, Jinan Shandong 250358, China
  • Received:2017-10-12 Revised:2017-11-15 Online:2018-04-10 Published:2018-04-09
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61672329), the Shandong Provincial Science and Technology Program (2014GGX101026), the Shandong Province Education Science Planning Program (ZK1437B010), the Graduate Scientific Research Innovation Fund of Shandong Normal University (SCX201747).

摘要: 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。

关键词: 认知风格, 多模态, 频繁模式, 记忆力, 眼动追踪, 网页广告

Abstract: 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.

Key words: cognitive style, multi-modality, frequent pattern, memory, eye-trackeing, Web advertisement

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