2015 | OriginalPaper | Buchkapitel
Benchmark of Rule-Based Classifiers in the News Recommendation Task
verfasst von : Tomáš Kliegr, Jaroslav Kuchař
Erschienen in: Experimental IR Meets Multilinguality, Multimodality, and Interaction
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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.