Abstract
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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Bridge, D., Kelleher, J. (2002). Experiments in Sparsity Reduction: Using Clustering in Collaborative Recommenders. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_18
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DOI: https://doi.org/10.1007/3-540-45750-X_18
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