基于SVD填充的混合推荐算法
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摘要
随着互联网技术的发展,信息过载问题日益严重,推荐系统是缓解该问题的有效手段。针对协同过滤中因数据稀疏和冷启动导致的推荐效率低下问题,提出基于SVD填充的混合推荐算法。首先,采用奇异值分解技术分解项目评分矩阵,通过随机梯度下降法填充稀疏矩阵;然后,在矩阵中加入时间权重,优化用户相似度,同时在项目矩阵中加入Jaccard系数优化项目相似度;接着,综合基于项目和基于用户的协同过滤计算预测评分,从而选择最优项目;最后,在MovieLens和Jester数据集中将所提算法与传统算法进行实验对比,证明了所提算法的有效性。
With the development of Internet technology,the issue of information overload is becoming increasingly se-rious.The recommendation system is an effective means to alleviate this problem.Focusing on the problem of low recommendation efficiency caused by sparse data and cold start in collaborative filtering,this paper proposed a hybrid recommendation algorithm based on SVD filling.Firstly,Singular Value Decomposition technique is used to decompose the user-item score matrix,and sparse matrix is filled by stochastic gradient descent method.Secondly,time weights are added to optimize the user similarity in the user matrix.At the same time,Jaccard coefficients are added to optimize the item similarity in the item matrix.Then,item-based and user-based collaborative filtering are combined to calculate prediction scores and select the optimal project.Finally,the proposed algorithm is compared with other existing algorithms on Movielens and Jester data set,and the result of experiments verifies that the effectiveness of the proposed algorithm.
With the development of Internet technology,the issue of information overload is becoming increasingly se-rious.The recommendation system is an effective means to alleviate this problem.Focusing on the problem of low recommendation efficiency caused by sparse data and cold start in collaborative filtering,this paper proposed a hybrid recommendation algorithm based on SVD filling.Firstly,Singular Value Decomposition technique is used to decompose the user-item score matrix,and sparse matrix is filled by stochastic gradient descent method.Secondly,time weights are added to optimize the user similarity in the user matrix.At the same time,Jaccard coefficients are added to optimize the item similarity in the item matrix.Then,item-based and user-based collaborative filtering are combined to calculate prediction scores and select the optimal project.Finally,the proposed algorithm is compared with other existing algorithms on Movielens and Jester data set,and the result of experiments verifies that the effectiveness of the proposed algorithm.
引文
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[14] REDDY M S,ADILAKSHMI T.Music recommendation system based on matrix factorization technique-SVD[C]//International Conference on Computer Communication and Informatics.IEEE,2014:1-6.
[15] WANG J,LI X,WU W,et al.An algorithm of collaborative filtering based on SVD and trust factors[J].Journal of Chinese Computer Systems,2017,38(6):1290-1293.
[16] VOZALIS M G,MARGARITIS K G.Applying SVD on item-based filtering[C]//International Conference on Intelligent Systems Design and Applications.IEEE,2005:464-469.
[17] ZHENG X,LUO Y,SUN L,et al.A new recommender systemusing context clustering based on matrix factorization techniques[J].Chinese Journal of Electronics,2016,25(2):334-340.
[18] ZHAO F,XIONG Y,LIANG X,et al.Privacy-preserving colla- borative filtering based on time-drifting characteristic[J].Chinese Journal of Electronics,2016,25(1):20-25.
1)http://MovieLens.umn.edu
[2] JANNACH D,NAVEED S,JUGOVAC M.User control in recommender systems:Overview and Interaction Challenges[C]//International Conference on Electronic Commerce and Web Technologies.2016:21-33.
[3] RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]//ACM Conference on Computer Supported Cooperative Work.ACM,1994:175-186.
[4] DAVIDSON J,LIEBALD B,LIU J,et al.The YouTube video recommendation system[C]//ACM Conference on Recommender Systems.ACM,2010:293-296.
[5] 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.
[6] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//International Conference on World Wide Web.ACM,2001:285-295.
[7] DESHPANDE M,KARYPIS G.Item-based top- N recommendation algorithms[J].ACM International Conference on Tran-sactions on Information Systems,2004,22(1):143-177.
[8] WU Q,LIN X,HE L.Unifying user-based and item-based algorithm to improve collaborative filtering accuracy[J].Energy Procedia,2011,13:8231-8239.
[9] WANG B,HUANG J,OU L,et al.A collaborative filtering algorithm fusing user-based,item-based and social networks[C]//IEEE International Conference on Big Data.IEEE,2015:2337-2343.
[10] ZHENG X,LUO Y,et al.Tourism destination recommender system for the cold start problem[J].KSII Transactions on Internet and Information Systems,2016,10(7):3192-3212.
[11] KANT S,MAHARA T.Merging user and item based collaborative filtering to alleviate data sparsity[J].International Journal of System Assurance Engineering and Management,2018,9(1):173-179.
[12] MA C C.A guide to singular value decomposition for collaboratative filtering[J].Computer,2009,42(3):30-37.
[13] KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].IEEE Computer,2009,42(1):30-37.
[14] REDDY M S,ADILAKSHMI T.Music recommendation system based on matrix factorization technique-SVD[C]//International Conference on Computer Communication and Informatics.IEEE,2014:1-6.
[15] WANG J,LI X,WU W,et al.An algorithm of collaborative filtering based on SVD and trust factors[J].Journal of Chinese Computer Systems,2017,38(6):1290-1293.
[16] VOZALIS M G,MARGARITIS K G.Applying SVD on item-based filtering[C]//International Conference on Intelligent Systems Design and Applications.IEEE,2005:464-469.
[17] ZHENG X,LUO Y,SUN L,et al.A new recommender systemusing context clustering based on matrix factorization techniques[J].Chinese Journal of Electronics,2016,25(2):334-340.
[18] ZHAO F,XIONG Y,LIANG X,et al.Privacy-preserving colla- borative filtering based on time-drifting characteristic[J].Chinese Journal of Electronics,2016,25(1):20-25.
1)http://MovieLens.umn.edu