Automatic text processing
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Learning while filtering documents
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using web helper agent profiles in query generation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Filtering for personal web information agents
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
QueryTracker: An Agent for Tracking Persistent Information Needs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Internet Technology (TOIT)
Hybrid personalized recommender system using centering-bunching based clustering algorithm
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
We present a lightweight text filtering algorithm intended for use with personal Web information agents. Fast response and low resource usage were the key design criteria, in order to allow the algorithm to run on the client side. The algorithm learns adaptive queries and dissemination thresholds for each topic of interest in its user profile. We describe a factorial experiment used to test the robustness of the algorithm under different learning parameters and more importantly, under limited training feedback. The experiment borrows from standard practice in TREC by using TREC-5 data to simulate a user reading and categorizing documents. Results indicate that the algorithm is capable of achieving good filtering performance, even with little user feedback.