• Analysis of police reports using emergent self-organizing maps and multidimensional scaling

      Poelmans, Jonas; Ignatov, Dmitry I.; Van Hulle, M.; Viaene, Stijn; Dedene, Guido (+); Elzinga, Paul (2011)
    • Concept relation discovery and innovation enabling technology

      Poelmans, Jonas; Elzinga, Paul; Neznanov, Alexei A.; Kuznetsov, Sergei O.; Dedene, Guido (+); Ignatov, Dmitry I.; Viaene, Stijn (2011)
    • A new cross-validation technique to evaluate quality of recommender systems

      Ignatov, Dmitry I.; Poelmans, Jonas; Dedene, Guido; Viaene, Stijn (2012)
      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.