基于自相似矩阵的协同过滤推荐算法
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摘要
针对推荐系统中存在的噪声问题,提出了一种基于自相似矩阵的协同过滤推荐算法.文中的自相似矩阵选取为原始矩阵,滑动窗口选取为评分值的行向量和列向量.通过建立评分值与自相似矩阵之间的线性关系,对原始评分矩阵进行预处理,得到新的评分矩阵.新评分矩阵既保留了原始矩阵的评分信息,同时也削弱了噪声数据对推荐系统的影响.实验表明,通过对原始矩阵的预处理,有效缓解了噪声数据在评分矩阵中所起的作用,提高了推荐系统的性能.
A collaborative filtering recommendation algorithm based on self-similar matrices is put forward for the noise problem in the proposed system.In this paper,self-similar matrices are selected as primitive matrices,and the sliding window is chosen as the row vector and column vector of the score.The new score matrix is obtained to preprocess the original scoring matrix to establish the linear relationship between the scoring value and the self-similar matrices.The new scoring matrix preserves the original matrix of scoring information,while weakening the impact of noise data on the recommended system.Experiments show that the pre-processing of the original matrix effectively alleviates the impact of noise in the scoring matrix and improves the performance of the proposed system.
A collaborative filtering recommendation algorithm based on self-similar matrices is put forward for the noise problem in the proposed system.In this paper,self-similar matrices are selected as primitive matrices,and the sliding window is chosen as the row vector and column vector of the score.The new score matrix is obtained to preprocess the original scoring matrix to establish the linear relationship between the scoring value and the self-similar matrices.The new scoring matrix preserves the original matrix of scoring information,while weakening the impact of noise data on the recommended system.Experiments show that the pre-processing of the original matrix effectively alleviates the impact of noise in the scoring matrix and improves the performance of the proposed system.
引文
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[2]CACHEDA F,FORMOSO V.Comparison of collalborative filtering algorithms:Limitations of current techniques and proposals for scalalble,high-performance recommender systems[J].Acm Transactions on the Web,2011,5(1):1-33.
[3]GUO G,ZHANG J,YORKE-SMITH N.TrustSVD:Collalborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]//Twenty-Ninth AAAI Conference on Artificial Intelligence.Palo Alto,CA,USA:AAAI Press,2015:123-129.
[4]SARWAR B,KARYPIS G,KONSTAN J,et al.Application of dimensionality reduction in recommender systemsA case study[C]//Proc of the Acm Webkdd Workshop.New York:ACM,2000.
[5]MASSA P,AVESANI P.Trust-aware collalborative filtering for recommender systems[M]//On the Move to Meaningful Internet Systems 2004,Coopis,DOA,and Odlbase.Berlin:Springer,2004:492-508.
[6]AMATRIAIN X,PUJOL J M,OLIVER N.I like it.I like it not:Evaluating user ratings noise in recommender systems[C]//International Conference on User Modeling,Adaptation,and Personalization:Formerly Um and Ah.Berlin:Springer-Verlag,2009,5535:247-258.
[7]XUE G R,LIN C,YANG Q,et al.Scalable collaborative filtering using cluster-based smoothing[C]//International Acm Sigir Conference on Research&Development in Information Retrieval.New York:ACM,2005:114-121.
[8]ZHANG Z K,ZHOU T,ZHANG Y C.Tag-aware recommender systems:A state-of-the-art survey[J].Journal of Computer Science and Technology,2011,26(5):767-777.
[9]UNGER M,BAR A,SHAPIRA B,et al.Towards latent context-aware recommendation systems[J].KnowledgeBased Systems,2016,104:165-178.
[10]CHIRITA P A,NEJDL W,ZAMFIR C.Preventing shilling attacks in online recommender systems[C]//ACM International Workshop on Web Information and Data Management.New York:ACM,2005:67-74.
[11]BILGE A,OZDEMIR Z,POLAT H.A novel shilling attack detection method[J].Procedia Computer Science,2014,31:165-174.
[12]刘江冬,梁刚,冯程,等.基于信息熵和时效性的协同过滤推荐[J].计算机应用,2016,36(9):2531-2534.
[13]MA H,KING I,LYU M R.Effective missing data prediction for collaborative filtering[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2007:39-46.
[14]朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.
[15]KOREN Y.The bellkor solution to the netflix grand prize[J].Netflix Prize Documentation,2009(8):1-10.