Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Agents that reduce work and information overload
Communications of the ACM
Experience with a learning personal assistant
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A reinforcement learning agent for personalized information filtering
Proceedings of the 5th international conference on Intelligent user interfaces
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Agent technology for personalized information filtering: the PIA-system
Proceedings of the 2005 ACM symposium on Applied computing
Incremental profile learning based on a reinforcement method
Proceedings of the 2005 ACM symposium on Applied computing
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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People spend an increasing amount of time using the web and the information sources. Some significant factors of that time are spent on navigation overhead like searching for relevant information through huge of irrelevant data retrieved. This paper presents RePLS, a Content-based reinforcement profile learning agent system that learns the user's interests by analyzing the contents of the documents, build his profiles and block the irrelevant documents by filtering the incoming documents according to the learned user's needs. The main idea is to select- when the documents is indexed, stemmed, represented and selected as relevant-the best terms representing the profile which help to discriminate between profiles. The agent updates the profile with every selected document to meet the user interests. The learning mechanism used by the agents is relevance feedback and reinforcement learning. Agent Foundation Classes suite AFC is used to build the proposed agents under RETSINA architecture. RePLS efficiency is measured by using the linear utility T11SU described by TREC evaluation which shows a better documents categorization than previous profile learning methods, namely query zoning and incremental profile learning based on the reinforcement method. The used documents are TREC 2002 filtering track collections.