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    A new cross-validation technique to evaluate quality of recommender systems

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    Publication type
    Conference Proceeding
    Author
    Ignatov, Dmitry I.
    Poelmans, Jonas
    Dedene, Guido
    Viaene, Stijn
    Editor
    Kundu, M.K.
    Mitra, S.
    Mazumdar, D.
    Pal, S.K.
    Publication Year
    2012
    Book
    Perception and machine intelligence. PerMIn 2012. Lecture notes in Computer Science
    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.
    Keyword
    Recommender Systems, Quality of Recommendations, User-behavior Modeling, Applied Combinatorics
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1007/978-3-642-27387-2 25
    URI
    http://hdl.handle.net/20.500.12127/6153
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-27387-2 25
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