计及Spark和属性权重的售电套餐推荐方法
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摘要
针对电力市场用户群庞大,交易过程中售电套餐选择困难的问题,在Spark环境下设计了一种售电套餐推荐方法,同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先,计算出每个套餐属性的权重值,从而计算得到售电套餐综合相似度。然后,计及用户和套餐两方面提出一种售电套餐推荐方法,实现售电套餐的精准推荐。实验表明,提出的推荐方法能够明显提高推荐的准确度,并且在分布式环境下具有良好的推荐效率和可扩展性。
Aiming at the huge user groups in the power market and the difficult selection of power selling packages in the trading process, a recommendation method of power selling packages based on Spark is proposed. At the same time, the scalability and real-time issues of the packages in the big data environment is also solved. First, the weight values of each package attribute are calculated, and the comprehensive similarity is calculated. Then, taking into account both the user and packages, a recommended method of packages is proposed to achieve the accurate recommendation of power selling packages. Experiments show that the proposed method can significantly improve the accuracy of recommendation and has good recommendation efficiency and scalability in distributed environment.
Aiming at the huge user groups in the power market and the difficult selection of power selling packages in the trading process, a recommendation method of power selling packages based on Spark is proposed. At the same time, the scalability and real-time issues of the packages in the big data environment is also solved. First, the weight values of each package attribute are calculated, and the comprehensive similarity is calculated. Then, taking into account both the user and packages, a recommended method of packages is proposed to achieve the accurate recommendation of power selling packages. Experiments show that the proposed method can significantly improve the accuracy of recommendation and has good recommendation efficiency and scalability in distributed environment.
引文
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[2]段俊祥.售电侧的互联网创新[J].低碳世界,2016,17:39-40.
[3]程云志.分析完美德州1820个售电套餐是一种什么体验[EB/OL].(2016-05-12)[2018-1-20].http://www.escn.com.cn/news/show-300044.html.
[4]张小平,李佳宁,付灏.英国电力零售市场的改革与挑战[J].电力系统自动化,2016,40(11):10-16.
[5]丁一,惠红勋,林振智,等.面向电力需求侧主动响应的商业模式及市场框架设计[J].电力系统自动化,2017,41(14):2-9.
[6]张玉洁,杜雨露,孟祥武.组推荐系统及其应用研究[J].计算机学报,2016,39(4):745-764.
[7]蔡玲,许珺,李奥勇.群体偏好增强的混合群推荐方法[J].计算机工程与应用,2017,53(9):5-10.
[8]谢霖铨,梁博群.结合用户特征分类和动态时间的协同过滤推荐[J].计算机工程与应用,2017,53(6):80-84.
[9]胡勋,孟祥武,张玉洁,等.一种融合项目特征和移动用户信任关系的推荐算法[J].软件学报,2014,25(8):1817-1830.
[10]于洪,李俊华.一种解决新项目冷启动问题的推荐算法[J].软件学报,2015,26(6):1395-1408.
[11]王明佳,韩景倜.基于用户对项目属性偏好的协同过滤算法[J].计算机工程与应用,2017,53(6):106-110.
[12]Zhong X,Yang G,Li L,et al.Clustering and correlation based collaborative filtering algorithm for cloud platform[J].IAENG International Journal of Computer Science,2016,43(1):108-114.
[13]孙天昊,黎安能,李明,等.基于Hadoop分布式改进聚类协同过滤推荐算法研究[J].计算机工程与应用,2015,51(15):124-128.
[14]Venkatesan T,Ramkumar T,Saravanan K.Mining big data:towards a machine learning framework based on collaborative filtering[J].International Journal of Control Theory&Applications,2017,10(12):69-77.
[15]García-Gil D,Ramírez-Gallego S,García S,et al.A comparison on scalability for batch big data processing on Apache Spark and Apache Flink[J].Big Data Analytics,2017,2(1):1-5.
[16]吴斌,肖琰,张云雷.基于Spark的并行增量动态社团发现算法[J].清华大学学报(自然科学版),2017,57(10):1030-1037.70 80 9070 80 90