Probabilistic Learning for Information Filtering

In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalised probabilistic model of Information Retrieval. The model is based on the concept of "uncertainty sampling" a technique that allows for relevance feedback both on relevant and non relevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile.

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