基于布谷鸟搜索的聚类推荐算法研究综述
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摘要
目前推荐系统研究面临的主要问题是如何提高推荐准确度和用户满意度。为克服原始推荐算法和现存改进算法的局限性,利用一种具有较强全局搜索能力的智能优化算法——布谷鸟搜索算法,结合K-means类算法进行改进。在此基础上,设计了应用于Movielens其中关键技术和目前存在问题进行了分析,并指出接下来需开展的研究工作。
The main problem faced by the current recommendation system research is how to improve the accuracy of recommendation and the satisfaction of users.In order to address the limitations of the original recommendation algorithm and the existing improved algorithm,this paper uses an intelligent optimization algorithm with strong global search ability——the cuckoo search algorithm,combined with the K-means clustering algorithm to make improvement.The overall framework of cluster recommendation system based on cuckoo search for Movielens dataset is designed.This paper analyzes the key technologies involved and the existing problems,and points out the research work that needs to be carried out next.
The main problem faced by the current recommendation system research is how to improve the accuracy of recommendation and the satisfaction of users.In order to address the limitations of the original recommendation algorithm and the existing improved algorithm,this paper uses an intelligent optimization algorithm with strong global search ability——the cuckoo search algorithm,combined with the K-means clustering algorithm to make improvement.The overall framework of cluster recommendation system based on cuckoo search for Movielens dataset is designed.This paper analyzes the key technologies involved and the existing problems,and points out the research work that needs to be carried out next.
引文
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[2]陈军,谢卫红,陈扬森.国内外大数据推荐算法领域前沿动态研究[J].中国科技论坛,2018(1):173-181.
[3]王晓耘,钱璐,黄时友.基于粗糙用户聚类的协同过滤推荐模型[J].现代图书情报技术,2015(1):45-51.
[4]喻金平,郑杰,梅宏标.基于改进人工蜂群算法的K-均值聚类算法[J].计算机应用,2014,34(4):1065-1069+1088.
[5]刘靖明,韩丽川,侯立文.基于粒子群的K均值聚类算法[J].系统工程理论与实践,2005,25(6):54-58.
[6]何宏,谭永红.一种基于动态遗传算法的聚类新方法[J].电子学报,2012,40(2):254-259.
[7]郭凯,李海芳,王会青.一种人工免疫的自适应谱聚类算法[J].小型微型计算机系统,2013,34(4):856-859.
[8]王彩霞.基于改进引力搜索的混合K-调和均值聚类算法研究[J].计算机应用研究,2016,33(1):118-121.
[9]王日宏,崔兴梅,李永珺.自适应调整的布谷鸟搜索K-均值聚类算法[J/OL].计算机应用研究,2018,35(12).[2017-12-08].http://www.arocmag.com/article/02-2018-12-038.html.
[10]任帅,王浙明,王明敏.基于用户行为模型和蚁群聚类的协同过滤推荐算法[J].微型电脑应用,2014(3):5-8.
[11]冯智明,苏一丹,覃华,等.基于遗传算法的聚类与协同过滤组合推荐算法[J].计算机技术与发展,2014(1):35-38.
[12]乔平安,曹宇,任泽乾.融合隐语义模型和K-meansplus聚类模型的推荐算法[J].计算机与数字工程,2018(6):1108-1111.
[13]丁小焕,彭甫镕,王琼,陆建峰.基于平行因子分解的协同聚类推荐算法[J].计算机应用,2016,36(6):1594-1598+1604.
[14]郑丹,王名扬,陈广胜.基于Weighted-slope One的用户聚类推荐算法研究[J].计算机技术与发展,2016(4):51-55.
[15]GANDOMI AH,YANG X S,ALAVI AH.Cuckoo search algorithm:a meta heuristic approach to solve structural optimization problems[J].Engineering with Computers,2013;29:17-35.
[16]李煜,马良.新型元启发式布谷鸟搜索算法[J].系统工程,2012,30(8):64-69.
[17]FENG D,QI R,LIMIN D U.Binary cuckoo search algorithm[J].Journal of Computer Applications,2013,33(6):1566-1570.
[18]SURESH S,LAL S,REDDY C S,et al.A novel adaptive cuckoo search algorithm for contrast enhancement of satellite images[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2017(99):1-12.
[19]YANG X S,DEB S.Cuckoo search via lévy flights[C].Nature&Biologically Inspired Computing,2009:210-214.
[20]YANG X S,DEB S.Engineering optimization by cuckoo search[J].International Journal of Mathematical Modelling and Numerical Optimization,2010,1(4):330-343.