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Information Retrieval systems can be improved by exploiting contextinformation such as user and document features. This article presents a modelbased on overlapping probabilistic or fuzzy clusters for such features. Themodel is applied within a fusion method which linearly combines severalretrieval systems. The fusion is based on weights for the different retrievalsystems which are learned by exploiting relevance feedback information. Thiscalculation can be improved by maintaining a model for each document and usercluster. That way, the optimal retrieval system for each document or user typecan be identified and applied. The extension presented in this article allowsoverlapping, probabilistic clusters of features to further refine the process.

Probability Based Clustering for Document and User Properties