Characterizing Typical and Atypical User Sessions in Clickstreams
Millions of users retrieve information from the Internet using search engines. Mining these user sessions can provide valuable information about the quality of user experience and the perceived quality of search results. Often search engines rely on accurate estimates of Click Through Rate (CTR) to evaluate the quality of user experience. The vast heterogeneity in the user population and presence of automated software programs (bots) can result in high variance in the estimates of CTR. To improve the estimation accuracy of user experience metrics like CTR, we argue that it is important identify typical and atypical user sessions in clickstreams. Our approach to identify these sessions is based on detecting outliers using Mahalanobis distance in the user session space. Our user session model incorporates several key clickstream characteristics including a novel conformance score obtained by Markov Chain analysis. Editorial results show that our approach of identifying typical and atypical sessions has a precision of about 89％. Filtering out these atypical sessions reduces the uncertainty (95％ confidence interval) of the mean CTR by about 40％. These results demonstrate that our approach of identifying typical and atypical user sessions is extremely valuable for cleaning “noisy user session data for increased accuracy in evaluating user experience.
Web Search Clickstream Analysis Outlier Detection
Narayanan Sadagopan Jie Li
Yahoo! 2821 Mission College Blvd Santa Clara, CA
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）