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Auflistung nach Autor:in "Rokach, Lior"

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  • Konferenzbeitrag
    A decision tree based recommender system
    (10th International Conferenceon Innovative Internet Community Systems (I2CS) – Jubilee Edition 2010 –, 2010) Gershman, Amir; Meisels, Amnon; Lüke, Karl-Heinz; Rokach, Lior; Schclar, Alon; Sturm, Arnon
    A new method for decision-tree-based recommender systems is proposed. The proposed method includes two new major innovations. First, the decision tree produces lists of recommended items at its leaf nodes, instead of single items. This leads to reduced amount of search, when using the tree to compile a recommendation list for a user and consequently enables a scaling of the recommendation system. The second major contribution of the paper is the splitting method for constructing the decision tree. Splitting is based on a new criterion - the least probable intersection size. The new criterion computes the probability for getting the intersection for each potential split in a random split and selects the split that generates the least probable size of intersection. The proposed decision tree based recommendation system was evaluated on a large sample of the MovieLens dataset and is shown to outperform the quality of recommendations produced by the well known information gain splitting criterion.
  • Konferenzbeitrag
    Recommenders benchmark framework
    (11th International Conference on Innovative Internet Community Systems (I2CS 2011), 2011) Dayan, Aviram; Katz, Guy; Lüke, Karl-Heinz; Rokach, Lior; Shapira, Bracha; Schwaiger, Roland; Aydin, Aykan; Fishel, Radmila; Biadsy, Nassem
    Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. Recommender systems have proven to be a valuable means for online users to cope with the virtual information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed during the last decade. In this paper we present a new benchmark framework. It allows researchers or practitioners to quickly try out and compare different recommendation methods on new data sets. Extending the framework is easy thanks to a simple and well-defined Application Programming Interface (API). It contains a plug-in mechanism allowing others to develop their own algorithms and incorporate them in the framework. An interactive graphical user interface is provided for setting new benchmarks, integrate new plug-ins with the framework, setting up configurations and exploring benchmark results.