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Ribeiro, M.T., Lacerda, A., Veloso, A. and Ziviani, N. (2012) Pareto-Efficient Hybridization for Multi-Objective Recommender Systems. Proceedings of the 6th ACM Conference on Recommender Systems, Dublin, 9-13 September 2012, 19-26.

has been cited by the following article:

• TITLE: Recommender Systems Based on Evolutionary Computing: A Survey

  AUTHORS: Maryam Sadeghi, Seyyed Amir Asghari

  KEYWORDS: Evolutionary Computing, Genetic Algorithm, Recommender System

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.10 No.5, May 25, 2017

  ABSTRACT: Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.