一种面向稀疏数据基于间接评分的协同过滤算法
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摘要
针对数据稀疏性问题,从提高稀疏数据矩阵利用效率这个角度,提出了一种基于间接评分的协同过滤算法,在基于用户和基于项目的协同过滤算法基础上,将2种算法的预测评分进行动态地混合加权作为直接预测评分,同时引入"相似用户"对"相似物品"的评分作为间接预测评分,最后把间接预测和直接预测2种评分加权形成用户对项目的最终评分.为证明该方法的有效性,使用MovieLens电影评分数据集对算法进行验证,结果表明该方法的平均绝对误差要比传统的基于用户和基于项目的协同过滤算法低,表明了在稀疏数据上该文提出的基于间接评分的协同过滤算法效果更佳.
Aiming at the problem of data sparsity, this paper proposes a collaborative filtering algorithm based on indirect scoring from the perspective of improving the efficiency of sparse data matrix. Based on the user-based and project-based collaborative filtering algorithm, the prediction scores of the two algorithms are used. Dynamically blending weights as direct predictive scores, while introducing "similar users" scores for "similar items" as indirect predictive scores, and finally weighting both indirect and direct predictions to form the user's final score for the project. In order to prove the validity of the proposed method, the MovieLens film scoring dataset is used to verify the algorithm. The experimental results show that the average absolute error of the proposed method is lower than the traditional user-based and project-based collaborative filtering algorithm which indicates that the collaborative filtering algorithm based on indirect scoring proposed in this paper is more effective on sparse data.
Aiming at the problem of data sparsity, this paper proposes a collaborative filtering algorithm based on indirect scoring from the perspective of improving the efficiency of sparse data matrix. Based on the user-based and project-based collaborative filtering algorithm, the prediction scores of the two algorithms are used. Dynamically blending weights as direct predictive scores, while introducing "similar users" scores for "similar items" as indirect predictive scores, and finally weighting both indirect and direct predictions to form the user's final score for the project. In order to prove the validity of the proposed method, the MovieLens film scoring dataset is used to verify the algorithm. The experimental results show that the average absolute error of the proposed method is lower than the traditional user-based and project-based collaborative filtering algorithm which indicates that the collaborative filtering algorithm based on indirect scoring proposed in this paper is more effective on sparse data.
引文
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[2] 朱扬勇,孙婧.推荐系统研究进展[J].计算机科学与探索,2015,9(5):513-525.
[3] Gong S J,Ye H W.An Item Based Collaborative Filtering Using BP Neural Networks Prediction[A].International Conference on Industrial & Information Systems[C],2009.
[4] Yamashita A,Kawamura H,Suzuki K.Adaptive fusion method for user-based and item-based collaborative fil tering[J].Advances in Complex Systems,2011,14(2):133-149.
[5] Zhou K.Combining item rating similarity and item classification similarity for better recommendation quality[J].Advanced Materials Research,2012,461:289-292.
[6] Naime Ranjbar Kermany,Sasan H Alizadeh.A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques[J].Electronic Commerce Research and Applications,2017,21:50-64.
[7] Fu M,Qu H,Yi Z,et al.A novel deep learning-based collaborative filtering model for recommendation system[J].IEEE Transactions on Cybernetics,2018,99:1-13.
[8] Jia Z,Lin Y,Lin M,et al.An effective collaborative filtering algorithm based on user preference clustering[J].Applied Intelligence,2016,45(2):230-240.
[9] Zhang Z,Kudo Y,Murai T.Neighbor selection for user-based collaborative filtering using covering-based rough sets[J].Annals of Operations Research,2017,256(2):359-374.
[10] Hernando A,Ortega F.A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model[J].Knowledge-Based Systems,2016,97(C):188-202.
[11] Li D,Chao C,Qin L,et al.An algorithm for efficient privacy-preserving item-based collaborative filtering[J].Future Generation Computer Systems,2016,55(C):311-320.
[12] Zheng X,He W,Li L.Distributed representations based collaborative filtering with reviews[J].Applied Intelligence,2019(4):1-18.
[13] Viana P.Tuning metadata for better movie content-based recommendation systems[J].Multimedia Tools & Applications,2015,74(17):7015-7036.
[14] Xiao J,Luo M,Chen J M,et al.An item based collaborative filtering system combined with genetic algorithms using rating behavior[J].Lecture Notes in Computer Science,2015,9227:453-460.
[15] Lei X,Jiang C,Yan C,et al.User participation in collaborative filtering-based recommendation systems:A game theoretic approach[J].IEEE Transactions on Cybernetics,2018,99:1-14.
[16] Zou J,Fekri F.A belief propagation approach to privacy-preserving item-based collaborative filtering[J].IEEE Journal of Selected Topics in Signal Processing,2015,9(7):1306-1318.