A New Cross-Validation Technique to Evaluate Quality of Recommender Systems
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.
Keywordsrecommender systems quality of recommendations user-behavior modeling applied combinatorics
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- 2.Sarwar, B.M., Karypis, J., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: ACM Conference on Electronic Commerce, pp. 158–167 (2001)Google Scholar
- 4.Ignatov, D.I., Kuznetsov, S.O.: Concept-based Recommendations for Internet Advertisement. In: Sixth International Conference on Concept Lattices and Their Applications, pp. 157–166. Palacky University, Olomouc (2008)Google Scholar
- 5.Ignatov, D.I., Kuznetsov, S.O.: Data Mining techniques for Internet Advertisement Recommender System. In: Proc. of 11th National Conference on Artificial Intelligence, Lenand, Moscow, vol. 2, pp. 34–42 (2008) (in Russian)Google Scholar
- 7.Segaran, T.: Programming Collective Intelligence. O’Reilly Media, Sebastopol (2007)Google Scholar
- 9.Poelmans, J., Elzinga, P., Viaene, S., Dedene, G.: Curbing domestic violence: instantiating C-K theory with formal concept analysis and emergent self-organizing maps. Int. Syst. in Accounting, Finance and Management 17(3-4), 167–191 (2010)Google Scholar