Email overload: exploring personal information management of email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
Taking email to task: the design and evaluation of a task management centered email tool
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
DIS '02 Proceedings of the 4th conference on Designing interactive systems: processes, practices, methods, and techniques
Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Exploiting query history for document ranking in interactive information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Designing remail: reinventing the email client through innovation and integration
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Implicit queries (IQ) for contextualized search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Just-in-time information retrieval agents
IBM Systems Journal
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
Improving proactive information systems
Proceedings of the 10th international conference on Intelligent user interfaces
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Y!Q: contextual search at the point of inspiration
Proceedings of the 14th ACM international conference on Information and knowledge management
Email overload at work: an analysis of factors associated with email strain
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Context-Aware, adaptive information retrieval for investigative tasks
Proceedings of the 12th international conference on Intelligent user interfaces
Generating summary keywords for emails using topics
Proceedings of the 13th international conference on Intelligent user interfaces
Social networks and discovery in the enterprise (SaND)
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Agent-assisted task management that reduces email overload
Proceedings of the 15th international conference on Intelligent user interfaces
EPIC: a multi-tiered approach to enterprise email prioritization
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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We present ICARUS, a contextual information retrieval system, which uses the current email message and a multi-tiered user model to retrieve relevant content and make it available in a sidebar widget embedded in the email client. The system employs a dynamic retrieval strategy to conduct automated contextual search across multiple information sources including the user's hard drive, online documents (wikis, blogs and files) and other email messages. It also presents the user with information about the sender of the current message, which varies in detail and degree based on how often the user interacts with this sender. We conducted a formative evaluation which compared three retrieval methods that used different context information: current message plus a multi-tiered user model; current message plus a single-tiered, aggregate user model; and lastly, cur-rent message only. Results indicate that the multi-tiered user modeling approach yields better retrieval performance than the other two. In addition, the study suggests that dynamically determining which sources to search, what query parameters to use, and how to filter/re-rank results can further improve the effectiveness of contextual IR.