research-article

Characterizing typical and atypical user sessions in clickstreams

Authors:

Narayanan Sadagopan,

Jie LiAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 885 - 894

https://doi.org/10.1145/1367497.1367617

Published: 21 April 2008 Publication History

Abstract

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.

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Dobashi K(2023)Sync Ratio and Cluster Heat Map for Visualizing Student EngagementEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_7(255-289)Online publication date: 30-Apr-2023
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Index Terms

Characterizing typical and atypical user sessions in clickstreams
1. Mathematics of computing
  1. Probability and statistics
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ACM: Association for Computing Machinery

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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ACM

WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TNET.2024.3392029
Yang XXu JLi G(2023)Efficient Fingerprinting Attack on Web Applications: An Adaptive Symbolization ApproachElectronics10.3390/electronics1213294812:13(2948)Online publication date: 4-Jul-2023
https://doi.org/10.3390/electronics12132948
Dobashi K(2023)Sync Ratio and Cluster Heat Map for Visualizing Student EngagementEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_7(255-289)Online publication date: 30-Apr-2023
https://doi.org/10.1007/978-981-99-0026-8_7
Tian DJuristo N(2021)Detecting user-perceived failure in mobile applications via mining user tracesProceedings of the 43rd International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion52605.2021.00054(123-125)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1109/ICSE-Companion52605.2021.00054
Yang DLi ZWang XSalamatian KXie G(2021)Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web SearchJournal of Computer Science and Technology10.1007/s11390-021-0218-236:5(1167-1183)Online publication date: 30-Sep-2021
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Schouten RBueno MDuivesteijn WPechenizkiy M(2021)Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functionsData Mining and Knowledge Discovery10.1007/s10618-021-00808-xOnline publication date: 24-Nov-2021
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Hatt TFeuerriegel S(2020)Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process ModelProceedings of The Web Conference 202010.1145/3366423.3380238(1671-1681)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380238
Zhang XLi ZZhu YLiu QZheng C(2020)Traceback: Learning to Identify Website’s Landing URLs via Noisy Web Traces Passively2020 27th International Conference on Telecommunications (ICT)10.1109/ICT49546.2020.9239491(1-6)Online publication date: 5-Oct-2020
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Olmezogullari EAktas M(2020)Representation of Click-Stream DataSequences for Learning User Navigational Behavior by Using Embeddings2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378437(3173-3179)Online publication date: 10-Dec-2020
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Bai GLien CChen H(2019)Co-learning Multiple Browsing Tendencies of a User by Matrix Factorization-based Multitask LearningIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352526(253-257)Online publication date: 14-Oct-2019
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