A learning approach to personalized information filtering
B. Sheth · DSpace@MIT (Massachusetts Institute of Technology) · 1994
A personalized information filtering system must specialize to current interests of the user and adapt as they change over time. It must also explore newer domains for potentially interesting information. A learning approach to building personalized information filtering systems is proposed. The system is designed as a collection of information filtering interface agents. Interface Agents are intelligent and autonomous computer programs which learn users' preferences and act on their behalf --- electronic personal assistants that automate tasks for the user. This thesis presents the basic framework for personalized information filtering agents, and describes an implementation, "Newt", built using the framework. Newt uses a keyword based filtering algorithm. The learning mechanisms used are relevance feedback and the genetic algorithm. The user interface is friendly and accessible to both naive as well as power users. Experimental results indicate that Newt can be personalized to serve ...