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© Springer-Verlag Berlin Heidelberg 2002. The high cardinality and sparsity of a collaborative recommender's dataset is a challenge to its efficiency. We generalise an existing clustering technique and apply it to a collaborative recommender's dataset to reduce cardinality and sparsity. We systematically test several variations, exploring the value of partitioning and grouping the data.


Conference paper

Publication Date





144 - 149