Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Circuit Analysis: Theory and Practice
Circuit Analysis: Theory and Practice
On the value of temporal information in information retrieval
ACM SIGIR Forum
Taskposé: exploring fluid boundaries in an associative window visualization
Proceedings of the 21st annual ACM symposium on User interface software and technology
Forward Decay: A Practical Time Decay Model for Streaming Systems
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Harnessing Wisdom of the Crowds Dynamics for Time-Dependent Reputation and Ranking
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
iMecho: an associative memory based desktop search system
Proceedings of the 18th ACM conference on Information and knowledge management
Understanding web browsing behaviors through Weibull analysis of dwell time
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Subgraph mining on directed and weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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The way in which a user interacts with her desktop while performing some task generates an information trail that can be used to identify the task context and the user's interests. This new information can in turn be fed back into the system to increase the level of support available to the user for both current and future tasks. In this paper we present research which analyses user-activity log files to explore how a user's activities evolve with time. Resources fall in and out of a task based on the user's mental model for tackling that task. We assign time-varying, importance and association values to each resource, based on the dwell-time and the resource-switching patterns exhibited by the user while browsing. Furthermore, we propose a new dynamic graph algorithm called OnlineActivityGraph which leverages on these values to generate document clusters and short-term user models. We further present a discussion about the encouraging results obtained from our preliminary experiments.