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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 368-386, DOI: https://doi.org/10.21307/ijssis-2017-763
License : (CC BY-NC-ND 4.0)
Received Date : 22-October-2014 / Accepted: 15-January-2015 / Published Online: 01-March-2015
This article introduces ensemble learning algorithms in recommender systems, and in boosting algorithm framework of this article, shows how to filter the basic recommendation
algorithm according to the characteristics of boosting algorithm. By comparing the rational choice of the two recommended boosting algorithm is applied to the frame. And then it determines the main parameters of the algorithm through the experiments, ultimately to obtain a more effective integration of the recommendation algorithm. Experimental results on Netflix validate the effectiveness of the proposed algorithm.
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