- A new cross-validation technique te evaluate quality of recommender systems
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Economics and Business (FEB)
- Amsterdam Business School Research Institute (ABS-RI)
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.
- go to publisher's site
- Proceedings title: Perception and machine intelligence
Place of publication: Berlin ; Heidelberg
Editors: M.K. Kundu, S. Mitra, D. Mazumdar, S.P. Pal
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