Reliability analysis for the behavior of web retrieval users

被引：0
|
作者：
Cen R.-W. [1 ]
Liu Y.-Q. [1 ]
Zhang M. [1 ]
Ru L.-Y. [1 ]
Ma S.-P. [1 ]
机构：
[1] State Key Laboratory of Intelligent Technology and Systems, Tsinghua University
来源：
Ruan Jian Xue Bao/Journal of Software | 2010年 / 21卷 / 05期
关键词：
Click reliability; User behavior; Web search engine system;
D O I：
10.3724/SP.J.1001.2010.03744
中图分类号：
学科分类号：
摘要：
Based on large scale click-through data, this paper study the interactive process between user and search engine, and derive user decision process. A comparative study between clicks on relevant results and non-relevant ones analyzes the reliability of individual user click-through behavior on search results. Three types of features are proposed and estimated for separating reliable user clicks from other ones. Experimental results show that the proposed method evaluates the reliability of user behaviors effectively based on click context features of Web search users. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1055 / 1066
页数：11
相关论文
共 20 条
  • [1] Baeza-Yates R., Tiberi A., Extracting semantic relations from query logs, Proc. of the 13th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD 2007), pp. 76-85, (2007)
  • [2] Fuxman A., Tsaparas P., Achan K., Agrawal R., Using the wisdom of the crowds for keyword generation, Proc. of the 17th Int'l Conf. on World Wide Web (WWW 2008), pp. 61-70, (2008)
  • [3] Joachims T., Freitag D., Mitchell T., WebWatcher: A tour guide for the world wide Web, Proc. of the 15th Int'l Joint Conf. on Artificial Intelligence, pp. 770-777, (1997)
  • [4] Agichtein E., Brill E., Dumais S., Ragno R., Learning user interaction models for predicting Web search result preferences, Proc. of the 29th Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2006), pp. 3-10, (2006)
  • [5] Joachims T., Granka L., Pan B., Hembrooke H., Gay G., Accurately interpreting clickthrough data as implicit feedback, Proc. of the 28th Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2005), pp. 154-161, (2005)
  • [6] Agichtein E., Brill E., Dumais S., Improving Web search ranking by incorporating user behavior information, Proc. of the 29th Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2006), pp. 19-26, (2006)
  • [7] Dou Z.C., Song R.H., Yuan X.J., Wen J.R., Are click-through data adequate for learning Web search rankings, Proc. of the 17th ACM Conf. on Information and Knowledge Management (CIKM 2008), pp. 73-8, (2008)
  • [8] Liu Y.Q., Cen R.W., Zhang M., Ru L.Y., Ma S.P., Automatic search engine evaluation based on user behavior analysis, Journal of Software, 19, 11, pp. 3023-3032, (2008)
  • [9] Tan P.N., Kumar V., Modeling of Web robot navigational patterns, Proc. of the ACM WebKDD Workshop, (2000)
  • [10] Tan P.N., Kumar V., Discovery of Web robot sessions based on their navigational patterns, Data Mining and Knowledge Discovery, 6, 1, pp. 9-35, (2002)
12