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  • 标题:Personalized Recommendation Method of Power Information Operation and Maintenance Knowledge Based on Spark
  • 本地全文:下载
  • 作者:Zhaoyang Qu ; Zhaoyang Qu ; Pengfei Xu
  • 期刊名称:Journal of Communications
  • 印刷版ISSN:1796-2021
  • 出版年度:2016
  • 卷号:11
  • 期号:8
  • 页码:785-791
  • DOI:10.12720/jcm.11.8.785-791
  • 语种:English
  • 出版社:ACADEMY PUBLISHER
  • 摘要:Power information operation and maintenance iaiowledge overload has become a pressing issue with the development of smart grid construction. The traditional personalized recommendation method cannot meet the demand of personalized recommendation of power information maintenance knowledge in big data environment. This paper proposes a method based on Spark which gives a personalized recommendation method of power information operation and maintenance knowledge. Firstly, an implicit rating mechanism is introduced, which can transform the learning behavior of users into implicit rating of power information operation and maintenance knowledge. Secondly, a personalized recommendation method combing knowledge features and user interests is designed. Finally, the personalized recommendation method, based on Spark, is applied to recommend power information operation and maintenance knowledge. The experimental results show that the method can effectively improve the accuracy and real-time of recommendation.
  • 关键词:Spark;power information operation and maintenance knowledge;personalized recommendation;implicit rating;collaborative filtering
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