A learning approach to personalized information filtering

A personalized information ltering 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 ltering systems is proposed. The system is designed as a collection of information ltering 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 ltering agents, and describes an implementation, \Newt", built using the framework. Newt uses a keyword based ltering 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 some of the news ltering needs of the user, in particular, those that are more regular and predictable. Relevance feedback is good for specializing to user interests. The genetic algorithm causes the system to adapt and explore for new types of information. This demonstrates that Interface Agents are a promising approach to the problem of designing personalized information ltering. Abstract A personalized information ltering 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 ltering systems is proposed. The system is designed as a collection of information ltering 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 ltering agents, and describes an implementation, \Newt", built using the framework. Newt uses a keyword based ltering 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 some of the news ltering needs of the user, in particular, those that are more regular and predictable. Relevance feedback is good for specializing to user interests. The genetic algorithm causes the system to adapt and explore for new types of information. This demonstrates that Interface Agents are a promising approach to the problem …

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