Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Implementation of the SMART Information Retrieval System
Implementation of the SMART Information Retrieval System
SIFT: a tool for wide-area information dissemination
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Task-based K-Support system: disseminating and sharing task-relevant knowledge
Expert Systems with Applications: An International Journal
Keeping Found Things Found: The Study and Practice of Personal Information Management: The Study and Practice of Personal Information Management
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Effective collaboration in fast-changing environment can put great dem ands on a collaborator's time. Therefore, information retrieval and filtering tools for these environments should impose as little on that time as possible. Not only should they exclude as many irrelevant documents as possible from those presented to the user (to avoid the time wasted sorting through and reading those documents), they should also minimize the user's effort in characterizing his or her information needs. The goal of the Task-based Information Distribution Environment (TIDE) system is to achieve these objectives by explicitly representing each collaborator's current task and using those representations to deliver documents that meet the information needs implied by those tasks. It does this by treating information gathering as a diagnosis problem, in which the situation (i.e., the current state of beliefs about various questions related to a task) leads probabilistically to test that will provide the most evidence toward reaching a diagnosis (i.e., a description of the documents most likely to be useful to that task). It encodes tasks as nodes in a Bayesian network, and computes document descriptions based on the probabilistic relationship among tasks and their corresponding information requirements.