Benchmark of Rule-Based Classifiers in the News Recommendation Task

  • Tomáš Kliegr
  • Jaroslav KuchařEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)


In this paper, we present experiments evaluating Association Rule Classification algorithms on on-line and off-line recommender tasks of the CLEF NewsReel 2014 Challenge. The second focus of the experimental evaluation is to investigate possible performance optimizations of the Classification Based on Associations algorithm. Our findings indicate that pruning steps in CBA reduce the number of association rules substantially while not affecting accuracy. Using only part of the data employed for the rule learning phase in the pruning phase may also reduce training time while not affecting accuracy significantly.


Recommender Association rules Rule learning Decision trees 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Multimedia and Vision Research GroupQueen Mary University of LondonLondonUK
  2. 2.Web Engineering Group, Faculty of Information TechnologyCzech Technical University in PraguePragueCzech Republic
  3. 3.Faculty of Informatics and Statistics, Department of Information and Knowledge EngineeringUniversity of Economics PraguePragueCzech Republic

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