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A New Cross-Validation Technique to Evaluate Quality of Recommender Systems

P. 195-202.
Ignatov D. I., Poelmans J., Dedene G., Viaene S.

The topic of recommender systems is rapidly gaining interest in the user-behaviour modeling research domain. Over the years, various recommender algorithms based on different mathematical models have been introduced in the literature. Researchers interested in proposing a new recommender model or modifying an existing algorithm should take into account a variety of key performance indicators, such as execution time, recall and precision. Till date and to the best of our knowledge, no general cross-validation scheme to evaluate the performance of recommender algorithms has been developed. To fill this gap we propose an extension of conventional cross-validation. Besides splitting the initial data into training and test subsets, we also split the attribute description of the dataset into a hidden and visible part. We then discuss how such a splitting scheme can be applied in practice. Empirical validation is performed on traditional user-based and item-based recommender algorithms which were applied to the MovieLens dataset.

В книге

A New Cross-Validation Technique to Evaluate Quality of Recommender Systems
Под науч. редакцией: M. K. Kundu, S. Mitra, D. Mazumdar et al. Vol. 7143. Berlin; Heidelberg: Springer, 2012.