一种改进相似度的协同过滤算法
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摘要
传统的相似度计算方法通过评分信息得出用户之间的相关关系,这些方法仅仅从用户评价信息考虑用户之间的相似度,使计算结果过于片面,在稀疏数据集中受较大影响,导致推荐结果的准确性有所降低。针对一般的协同过滤推荐算法中存在的数据稀疏性问题,通过引入用户相似度权重系数,将Pearson相关系数进行加权处理后与Jaccard相似性方法相结合,提出一种新的计算方案,改进算法考虑了用户对共同评分项目所占的比率和用户对项目的评分取值大小,优化了协同过滤算法中相似度量的关键性能。在MovieLens和Book-Crossing两个公共数据集中进行试验,结果表明,改进算法使平均绝对误差值最大程度上降低了5. 2%,从而有效降低稀疏数据集对推荐结果的影响,显著提升了推荐系统的准确度。
Traditional similarity calculation methods draw correlation information between users through scoring information,these methods only consider the similarity between users from user evaluation information,and the sparse data sets can influence correctness of the recommendations in the recommendation system. Aiming to the problem of data sparsity in the general collaborative filtering recommendation algorithm,an improved similarity collaborative filtering algorithm was proposed. In the scheme,the user similarity weight coefficient was introduced,and the Pearson correlation coefficient was weighted and combined with the Jaccard similarity method.In this way,the key performance of the similarity metric algorithm in the collaborative filtering algorithm was improved by the scheme. The experiments in the public data sets( MovieLens and Book-Crossing set) showed that the improved algorithm could reduce the mean absolute error by 5. 2% to the maximum. The improved algorithm could also reduce the influence of sparse data set on the recommendation accuracy greatly,and improved the accuracy of the recommendation system significantly.
Traditional similarity calculation methods draw correlation information between users through scoring information,these methods only consider the similarity between users from user evaluation information,and the sparse data sets can influence correctness of the recommendations in the recommendation system. Aiming to the problem of data sparsity in the general collaborative filtering recommendation algorithm,an improved similarity collaborative filtering algorithm was proposed. In the scheme,the user similarity weight coefficient was introduced,and the Pearson correlation coefficient was weighted and combined with the Jaccard similarity method.In this way,the key performance of the similarity metric algorithm in the collaborative filtering algorithm was improved by the scheme. The experiments in the public data sets( MovieLens and Book-Crossing set) showed that the improved algorithm could reduce the mean absolute error by 5. 2% to the maximum. The improved algorithm could also reduce the influence of sparse data set on the recommendation accuracy greatly,and improved the accuracy of the recommendation system significantly.
引文
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[14]李容,李明奇,郭文强.基于改进相似度的协同过滤算法研究[J].计算机科学,2016,43(12):206-208+240.LI R,LI M Q,GUO W Q.Research on collaborative filtering algorithm with improved similarity[J].Computer Science,2016,43(12):206-208+240.
[2]房璐璐.基于大数据分析的推荐系统研究[D].北京:北京邮电大学,2015.FANG L L.Research on recommender system based on big data analysis[D].Beijing:Beijing University of Posts and Telecommunications,2015.
[3]荣辉桂,火生旭,胡春华,等.基于用户相似度的协同过滤推荐算法[J].通信学报,2014,35(2):16-24.RONG H J,HUO S X,HU C H,et al.User similaritybased collaborative filtering recommendation algorithm[J].Journal on Communications,2014,35(2):16-24.
[4]林耀进,张佳,林梦雷,等.一种基于模糊信息熵的协同过滤推荐方法[J].山东大学学报(工学版),2016,46(5):13-20.LIN Y J,ZHANG J,LIN M L,et al.A method of collaborative filtering recommendation based on fuzzy information entropy[J].Journal of Shandong University(Engineering Science),2016,46(5):13-20.
[5]ZHENG M,MIN F,ZHANG H R,et al.Fast recommendations with the M-distance[J].IEEE Access,2016,4:1464-1468.
[6]钟川,陈军.基于精确欧氏局部敏感哈希的改进协同过滤推荐算法[J].计算机工程,2017,43(2):74-78.ZHONG C,CHEN J.Improved collaborative filtering recommendation algorithm based on exact Euclidean locality sensitive hashing[J].Computer Engineering.2017,43(2):74-78.
[7]吴毅涛,张兴明,王兴茂,等.基于用户模糊相似度的协同过滤算法[J].通信学报,2016.37(1):198-206.WU Y T,ZHANG X M,WANG X M,et al.User fuzzy similarity-based collaborative filtering recommendation algorithm[J].Journal on Communications,2016,37(1):198-206.
[8]AR Y,BOSTANCI E.A genetic algorithm solution to the collaborative filtering problem[J].Expert Systems with Applications,2016,6(1):122-128.
[9]仇国庆,马俊,赵婉滢,等.融合正态分布函数相似度的协同过滤算法[J].计算机应用研究,2018,35(10):130-137.QIU G Q,MA J,ZHAO W Y,et al.Collaborative filtering algorithm fusing similarity of normal distribution function[J].Application Research of Computers,2018,35(10):130-137.
[10]何洁月,马贝.利用社交关系的实值条件受限玻尔兹曼机协同过滤推荐算法[J].计算机学报,2016(1):183-195.HE J Y,MA B.Based on real-valued conditional restricted Boltzmann machine and social network for collaborative filtering[J].Chinese Journal of Computers,2016(1):183-195.
[11]ZE W,DENGWEN Z.Optimization collaborative filtering recommendation algorithm based on ratings consistent[C]//Proc of the 7th IEEE International Conference on Software Engineering&Service Science.Beijing:The Institute of Electrical and Electronics Engineers,IEEE Beijing Section Communications Society,Beijing University of Technology,2016:1055-1058.
[12]李斌,张博,刘学军,等.基于Jaccard相似度和位置行为的协同过滤推荐算法[J].计算机科学,2016,43(12):200-205.LI B,ZHANG B,LIU X J,et al.Collaborative filtering recommendation algorithm based on Jaccard similarity and locational behaviors[J].Computer Science,2016,43(12):200-205.
[13]谢毅刚,郭卫斌,李建华.基于双层相似度的协同过滤推荐算法[J].华东理工大学学报(自然科学版)2018.44(1):131-138.XIE Y G,GUO W B,LI J H.Collaborative filtering algorithm based on bi-level similarity[J].Journal of East China University of Science and Technology(Natural Science Edition),2018,44(1):131-138.
[14]李容,李明奇,郭文强.基于改进相似度的协同过滤算法研究[J].计算机科学,2016,43(12):206-208+240.LI R,LI M Q,GUO W Q.Research on collaborative filtering algorithm with improved similarity[J].Computer Science,2016,43(12):206-208+240.