A new cross-validation technique to evaluate quality of recommender systems
Publication type
Conference ProceedingPublication Year
2012Book
Perception and machine intelligence. PerMIn 2012. Lecture notes in Computer SciencePublication Begin page
195Publication End page
202
Metadata
Show full item recordAbstract
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.Keyword
Recommender Systems, Quality of Recommendations, User-behavior Modeling, Applied CombinatoricsKnowledge Domain/Industry
Operations & Supply Chain Managementae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-27387-2 25