搜索引擎结果页面(SERP)研究述评
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  • 英文篇名:A Review on the Search Engine Result Page(SERP)
  • 作者:吴丹 ; 唐源
  • 英文作者:Wu Dan;Tang Yuan;School of Information Management, Wuhan University;
  • 关键词:搜索引擎结果页面 ; SERP ; 用户搜索行为 ; 用户注意力
  • 英文关键词:search engine result page;;SERP;;user seeking behavior;;user attention
  • 中文刊名:QBXB
  • 英文刊名:Journal of the China Society for Scientific and Technical Information
  • 机构:武汉大学信息管理学院;
  • 出版日期:2018-02-24
  • 出版单位:情报学报
  • 年:2018
  • 期:v.37
  • 基金:国家自然科学基金面上项目“多设备融合的用户网络搜索行为建模与技术实现研究”(71673204)
  • 语种:中文;
  • 页:QBXB201802010
  • 页数:11
  • CN:02
  • ISSN:11-2257/G3
  • 分类号:104-114
摘要
本文以国外会议、期刊发表的有关搜索引擎结果页面研究文献为对象,采用综合归纳的方法,分析该领域研究进展。研究发现:一方面,在搜索引擎结果页面的设计布局上,现代搜索聚合各类型信息资源,搜索结果页面呈现垂直化、多样化特征,新型页面元素的嵌入影响用户对结果页面的满意度、关注、体验和评价;另一方面,利用用户在搜索结果页面的行为数据,包括眼球、光标、手势、声学等各类交互数据,可以分析用户在搜索结果页面的关注点和注意力分布特征,建立用户注意力预测模型,实现对用户意图、注意力、结果偏向的预测。未来趋势包括:搜索引擎结果页面设计研究扩展到语音交互、社交网络等领域,更全面的交互数据将支持用户搜索行为建模和注意力预测。
        This paper uses synthesis methods to examine foreign conference and journal articles that study search engine result page(SERP), and analyzes the research progress in this field. With respect to the research available on the layout design of SERPs, the results show that since the modern search engine aggregates the various types of information resources, the SERP is vertical and diversified in nature, and the embedded elements of the SERP affect the user's satisfaction, attention, experience and evaluation of the SERP; on the other hand, using user-behavior data that includes eye movement, cursor movement, gestures, acoustics and other interactive data, it is possible to analyze the user-fixation areas and attention-distribution characteristics of the SERP to establish a user-attention prediction model, and to predict user intentions, attention, and the results of bias. Future directions mentioned in the literature include SERP layout design research extending to voice interaction, social networking, and other related fields, as well as more comprehensive interactive data support for user-seeking behavior modeling and attention prediction.
引文
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