Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Nearest-biclusters collaborative filtering based on constant and coherent values
Information Retrieval
Evaluation of recommender systems: A new approach
Expert Systems with Applications: An International Journal
Programming collective intelligence
Programming collective intelligence
International Journal of Intelligent Systems in Accounting and Finance Management
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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.