Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
An Adaptive Agent for Automated Web Browsing
An Adaptive Agent for Automated Web Browsing
Semantic Log Analysis Based on a User Query Behavior Model
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Identifying the optimal set of parameters for new topic identification through experimental design
Expert Systems with Applications: An International Journal
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Moving towards adaptive search in digital libraries
NLP4DL'09/AT4DL'09 Proceedings of the 2009 international conference on Advanced language technologies for digital libraries
User's Behaviour inside a Digital Library
International Journal of Decision Support System Technology
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Incremental learning approaches based on user search activities provide a means of building adaptive information retrieval systems. To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we run a high risk of grouping together activities that are unrelated or perhaps not from the same user. We are interested in detecting boundaries of sequences between related activities (sessions) that would group the activities for a learning purpose. Session boundaries, in Reuters transaction logs, were detected automatically. The generated boundaries were compared with human judgements. The comparison confirmed that a meaningful session threshold for establishing these session boundaries was confined to a 11-15 minute range.