基于奇异值分解和项目属性的推荐算法
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摘要
为了解决评分数据的稀疏性和用户最近邻的精确性问题,文章提出了一种基于奇异值分解(singular value decomposition,SVD)和项目属性的协同过滤推荐算法。该算法首先采用SVD方法对用户-项目评分矩阵降维,得到用户矩阵和项目矩阵,根据项目矩阵计算项目间的评分相似度,同时根据项目属性计算项目间的属性相似度,将2种相似度的结果加权计算得到项目间的相似度,最后采用最近邻的方法预测目标用户对待评分项目的评分。在MovieLens数据集上的实验结果表明,该文所提出的方法可以有效应对用户评分稀疏的问题,并能提高推荐的准确性。
In order to solve the problems of the sparsity of the scoring data and the accuracy of the user nearest neighbor,a collaborative filtering recommendation algorithm based on the singular value decomposition(SVD)and item attributes is proposed.Firstly,the SVD method is used to reduce the dimension of the user-item scoring matrix.Then the user matrix and the item matrix are obtained.The similarity between the items is calculated according to the scoring similarity between the items obtained by the item matrix and the attribute similarity by item attributes.Finally,the nearest neighbor method is used to predict the score of the target user to the target item.The results of the experiment on MovieLens dataset show that the proposed method can effectively solve the problem of data sparsity,and improve the accuracy of the recommendation.
In order to solve the problems of the sparsity of the scoring data and the accuracy of the user nearest neighbor,a collaborative filtering recommendation algorithm based on the singular value decomposition(SVD)and item attributes is proposed.Firstly,the SVD method is used to reduce the dimension of the user-item scoring matrix.Then the user matrix and the item matrix are obtained.The similarity between the items is calculated according to the scoring similarity between the items obtained by the item matrix and the attribute similarity by item attributes.Finally,the nearest neighbor method is used to predict the score of the target user to the target item.The results of the experiment on MovieLens dataset show that the proposed method can effectively solve the problem of data sparsity,and improve the accuracy of the recommendation.
引文
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[7]张佳,林耀进,林梦雷,等.基于目标用户近邻修正的协同过滤算法[J].模式识别与人工智能,2015,28(9):802-810.
[8]王庆庆,吴共庆,李磊.结合相似度量和反馈调节的推荐算法[J].合肥工业大学学报(自然科学版),2015,38(9):1210-1214.
[9]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.
[10]龚安,高云,高洪福.一种基于项目属性评分的协同过滤推荐算法[J].计算机工程与科学,2015,37(12):2366-2371.
[11]SARWAR B,KARYPIS G,KONSTAN J,et al.Itembased collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web.New York:ACM,2001:285-295.
[12]BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J].Knowledge-Based Systems,2013,46:109-132.
[13]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009(8):30-37.
[14]刘建国,周涛,郭强,等.个性化推荐系统评价方法综述[J].复杂系统与复杂性科学,2009,6(3):1-10.
[2]SARWAR B,KARYPIS G,KONSTAN J,et al.Application of dimensionality reduction in recommender system:a case study:TR00-043[R].Minneapolis:University of Minnesota,2000.
[3]汪静,印鉴,郑利荣,等.基于共同评分和相似性权重的协同过滤推荐算法[J].计算机科学,2010,37(2):99-104.
[4]黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8):1369-1377.
[5]孙小华,陈洪,孔繁胜.在协同过滤中结合奇异值分解与最近邻方法[J].计算机应用研究,2006,23(9):206-208.
[6]李聪,梁昌勇,董珂.基于项目类别相似性的协同过滤推荐算法[J].合肥工业大学学报(自然科学版),2008,31(3):360-363.
[7]张佳,林耀进,林梦雷,等.基于目标用户近邻修正的协同过滤算法[J].模式识别与人工智能,2015,28(9):802-810.
[8]王庆庆,吴共庆,李磊.结合相似度量和反馈调节的推荐算法[J].合肥工业大学学报(自然科学版),2015,38(9):1210-1214.
[9]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.
[10]龚安,高云,高洪福.一种基于项目属性评分的协同过滤推荐算法[J].计算机工程与科学,2015,37(12):2366-2371.
[11]SARWAR B,KARYPIS G,KONSTAN J,et al.Itembased collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web.New York:ACM,2001:285-295.
[12]BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J].Knowledge-Based Systems,2013,46:109-132.
[13]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009(8):30-37.
[14]刘建国,周涛,郭强,等.个性化推荐系统评价方法综述[J].复杂系统与复杂性科学,2009,6(3):1-10.