A new cross-validation technique to evaluate quality of recommender systems
Ignatov, Dmitry I. Poelmans, Jonas Dedene, Guido Viaene, Stijn
Ignatov, Dmitry I.
Poelmans, Jonas
Dedene, Guido
Viaene, Stijn
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Publication Type
Conference Proceeding
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Publication Year
2012
Journal
Book
Perception and machine intelligence. PerMIn 2012. Lecture notes in Computer Science
Publication Volume
Publication Issue
Publication Begin page
195
Publication End page
202
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Abstract
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.
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Keywords
Recommender Systems, Quality of Recommendations, User-behavior Modeling, Applied Combinatorics