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The Scientific World Journal
Volume 2014 (2014), Article ID 649596, 11 pages
http://dx.doi.org/10.1155/2014/649596
Research Article

Aggregated Recommendation through Random Forests

1School of Computer Science, Southwest Petroleum University, Chengdu 610500, China
2Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China

Received 23 May 2014; Accepted 23 July 2014; Published 11 August 2014

Academic Editor: Marco Listanti

Copyright © 2014 Heng-Ru Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Yanxue Wu, Xianjie Wang, and Zhiheng Zhang, “A high risk association rule mining algorithm for the petroleum industry,” Journal of Information and Computational Science, vol. 12, no. 12, pp. 4633–4642, 2015. View at Publisher · View at Google Scholar
  • Heng-Ru Zhang, and Fan Min, “Three-way recommender systems based on random forests,” Knowledge-Based Systems, 2015. View at Publisher · View at Google Scholar
  • Xinling Dong, Hengru Zhang, Ji Dong, and Fan Min, “Mining browsing user interests through user-recommender interaction,” Journal of Computational Information Systems, vol. 11, no. 11, pp. 4101–4111, 2015. View at Publisher · View at Google Scholar
  • Jian Li, and Xiao Pu, “A bayesian approach to mine accident causes association rules in petroleum drilling,” Journal of Information and Computational Science, vol. 12, no. 9, pp. 3475–3484, 2015. View at Publisher · View at Google Scholar
  • Heng-Ru Zhang, Fan Min, Xu He, and Yuan-Yuan Xu, “A Hybrid Recommender System Based on User-Recommender Interaction,” Mathematical Problems in Engineering, vol. 2015, pp. 1–11, 2015. View at Publisher · View at Google Scholar
  • Heng-Ru Zhang, Fan Min, and Bing Shi, “Regression-based three-way recommendation,” Information Sciences, 2016. View at Publisher · View at Google Scholar