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  • 标题:A general framework for intelligent recommender systems
  • 本地全文:下载
  • 作者:Jose Aguilar ; Priscila Valdiviezo-Díaz ; Guido Riofrio
  • 期刊名称:Applied Computing and Informatics
  • 印刷版ISSN:2210-8327
  • 电子版ISSN:2210-8327
  • 出版年度:2017
  • 卷号:13
  • 期号:2
  • 页码:147-160
  • DOI:10.1016/j.aci.2016.08.002
  • 出版社:Elsevier
  • 摘要:In this paper, we propose a general framework for an intelligent recommender system that extends the concept of a knowledge-based recommender system. The intelligent recommender system exploits knowledge, learns, discovers new information, infers preferences and criticisms, among other things. For that, the framework of an intelligent recommender system is defined by the following components: knowledge representation paradigm, learning methods, and reasoning mechanisms. Additionally, it has five knowledge models about the different aspects that we can consider during a recommendation: users, items, domain, context and criticisms. The mix of the components exploits the knowledge, updates it and infers, among other things. In this work, we implement one intelligent recommender system based on this framework, using Fuzzy Cognitive Maps (FCMs). Next, we test the performance of the intelligent recommender system with specialized criteria linked to the utilization of the knowledge in order to test the versatility and performance of the framework.
  • 关键词:Recommender system ; Cognitive maps
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