Creating user interfaces using programming by example, visual programming, and constraints
ACM Transactions on Programming Languages and Systems (TOPLAS)
Triggers and barriers to customizing software
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Watch what I do: programming by demonstration
Watch what I do: programming by demonstration
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Programming by demonstration: an inductive learning formulation
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
MailCat: an intelligent assistant for organizing e-mail
Proceedings of the third annual conference on Autonomous Agents
Representation of electronic mail filtering profiles: a user study
Proceedings of the 5th international conference on Intelligent user interfaces
Programming by example: novice programming comes of age
Communications of the ACM
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An experimental framework for email categorization and management
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Email answering assistance by semi-supervised text classification
Intelligent Data Analysis
| Hi-index | 0.00 |
Every regular email user is well aware of the problems for managing large numbers of messages in his/her mailbox, because of the large amount of email that people receive every day. As we knwo the most users feel difficult to maintain filter rule for email classification problem, even expert users still have to put some effort on the task. It will be valuable if users can have a smart system to help them maintaining email filter rule. We thus integrated: machine learning for defining the rules, scrutable model, and programming by demonstration, called the Scrutable Rule Interface, and added them into a part of IEMS, to show how they can encourage and assist users to manage filter rule for email management. We describe a practical assistance interface and present empirical results that found general and expert users feel more confidant to infer thier own rules or apply other mechanizms used to infer the filter rules.This improved performance to acceptable levels.