Online text retrieval via browsing
Information Processing and Management: an International Journal
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Web search behavior of Internet experts and newbies
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
In search of invariants for e-business workloads
Proceedings of the 2nd ACM conference on Electronic commerce
ACM SIGKDD Explorations Newsletter
Analyzing robot behavior in e-business sites
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
LA-WEB '05 Proceedings of the Third Latin American Web Congress
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Crawler Detection: A Bayesian Approach
ICISP '06 Proceedings of the International Conference on Internet Surveillance and Protection
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Robust methodologies for modeling web click distributions
Proceedings of the 16th international conference on World Wide Web
An investigation of web crawler behavior: characterization and metrics
Computer Communications
Stratified analysis of AOL query log
Information Sciences: an International Journal
Exploring relevance for clicks
Proceedings of the 18th ACM conference on Information and knowledge management
Study on the Click Context of Web Search Users for Reliability Analysis
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Large-scale bot detection for search engines
Proceedings of the 19th international conference on World wide web
Are search engine users equally reliable?
Proceedings of the 19th international conference on World wide web
Query suggestion for E-commerce sites
Proceedings of the fourth ACM international conference on Web search and data mining
You are how you click: clickstream analysis for Sybil detection
SEC'13 Proceedings of the 22nd USENIX conference on Security
Search engine click spam detection based on bipartite graph propagation
Proceedings of the 7th ACM international conference on Web search and data mining
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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 to 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.