Abstract
Users search and browse activity mined with special toolbars is known to provide diverse valuable information for the search engine. In particular, it helps to understand information need of a searcher, her personal preferences, context of the topic she is currently interested in. Most of the previous studies on the topic either considered the whole user activity for a fixed period of time or divided it relying on some predefined inactivity time-out. It helps to identify groups of web sites visited with the same information need. This paper addresses the problem of automatic segmentation of users browsing logs into logical segments. We propose a method for automatic division of their daily activity into intent-related parts. This segmentation advances the commonly used approaches. We propose several methods for browsing log partitioning and provide detailed study of their performance. We evaluate all algorithms and analyse contributions of various types of features.
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References
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., Vigna, S.: The Query-flow Graph: Model and Applications. In: CIKM (2008)
Friedman, J.H.: Greedy Function Approximation: A Gradient Boosting Machine
Jones, R., Klinkner, K.L.: Beyond the Session Timeout: Automatic Hi- erarchical Segmentation of Search Topics in Query Logs. In: CIKM (2008)
Hassan, A., Jones, R., Klinkner, K.L.: Beyond DCG: User Behavior as a Predictor of a Successful Search. In: WSDM (2010)
Ageev, M., Guo, Q., Lagun, D., Agichtein, E.: Find It If You Can: A Game for Modeling Different Types of Web Search Success Using Interaction Data. In: SIGIR (2011)
Poblete, B., Castillo, C., Gionis, A.: Dr. Searcher and Mr. Browser: a unified hyperlink-click graph. In: CIKM (2008)
Bilenko, M., White, R.W.: Mining the search trails of surfing crowds: identifying relevant websites from user activity. In: WWW (2008)
White, R.W., Huang, J.: Assessing the scenic route: measuring the value of search trails in web logs. In: SIGIR (2010)
Singla, A., White, R., Haung, J.: Studying trailfinding algorithms for enhanced web search. In: SIGIR (2010)
Guo, Q., White, R.W., Zhang, Y., Anderson, B., Dumais, S.T.: Why Searchers Switch: Understanding and Predicting Engine Switching Rationales. In: SIGIR (2011)
Wang, C.-J., Lin, K.H.-Y., Chen, H.-H.: Intent boundary detection in search query logs. In: SIGIR (2012)
Catledge, L., Pitkow, J.: Characterizing browsing strategies in the world-wide web. In: International World-Wide Web Conference on Technology, Tools and Applications (1995)
Anick, P.: Using terminological feedback for web search refinement — a log-based study. In: SIGIR (2003)
WSCD2012: Workshop on Web Search Click Data (2012)
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Ustinovskiy, Y., Mazur, A., Serdyukov, P. (2013). Intent-Based Browse Activity Segmentation. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_21
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DOI: https://doi.org/10.1007/978-3-642-36973-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
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