计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 50-56.doi: 10.11896/jsjkx.210600062
孙晓寒, 张莉
SUN Xiao-han, ZHANG Li
摘要: 协同过滤推荐算法因其合理的可解释性以及简单的实现过程而被广泛应用。然而,在推荐系统中数据集通常具有规模大、稀疏度和维度高等特点,这些特点给协同过滤推荐算法带来了很大的挑战。为了缓解上述问题,提出了一种基于评分区域子空间的协同过滤推荐算法。基于用户-项目评分矩阵,该算法首先将评分范围划分为3个区域,即高评分区域、中评分区域以及低评分区域,根据这3个区域分别为每个用户寻找其项目子空间,即高评分子空间、中评分子空间以及低评分子空间。其次,定义了一种新的相似度计算方式,在各区域子空间中分别计算用户之间的评分支持度,只有当用户在各个子空间上的评分支持度都很高时,用户之间才是相似的。这种方式避免了惰性评分用户的评分干扰。实验结果表明,该算法能够在一定程度上解决数据稀疏性问题,特别是针对高维数据能降低其计算复杂度,并提高其推荐性能。
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[1]BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J].Knowledge Based Systems,2013,46:109-132. [2]PATEL K,PATEL H B.A state-of-the-art survey on recommendation system and prospective extensions[J].Computers and Electronics in Agriculture,2020,178:105779. [3]STOCKLI D R,KHOBZI H.Recommendation systems and convergence of online reviews:The type of product network matters![J].Decision Support Systems,2021,142:113475. [4]COVINGTON P,ADAMS J,SARGIN E.Deep Neural Net-works for YouTube Recommendations[C]//Proceedings of the 10th Conference on Recommender Systems.Boston:ACM,2016:191-198. [5]KRAUSE A E,NORTH A C,HERITAGE B.The uses andgratifications of using Facebook music listening applications[J].Computers in Human Behavior,2014,39:71-77. [6]GARCIA I,SEBASTIA L,ONAINDIA E.On the design of individual and group recommender systems for tourism[J].Expert Systems with Applications,2011,38(6):7683-7692. [7]SHEN J,ZHOU T Q,CHEN L.Collaborative filtering-based recommendation system for big data[J].International Journal of Computational Science and Engineering,2020,21(2):219-225. [8]ALQADAH F,REDDY C K,HU J L,et al.Biclustering neighborhood-based collaborative filtering method for top-n recommender systems[J].Knowledge and Information Systems,2015,44(2):475-491. [9]SU X Y,KHOSHGOFTAAR T M.A Survey of Collaborative Filtering Techniques[J].Advances in Artificial Intelligence,2009,2009(421425):1-19. [10]ADOMAVICIUS G,TUZHILIN A.Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749. [11]XIAO C W,YE J Q,ESTEVES R M,et al.Using Spearman's correlation coefficients for exploratory data analysis on big dataset[J].Concurrency and Computation:Practice and Experience,2016,28(14):3866-3878. [12]BAG S,KUMAR S K,TIWARI M K.An efficient recommendation generation using relevant Jaccard similarity[J].Information Sciences,2019,483:53-64. [13]GARANAYAK M,MOHANTY S N,JAGADEV A K,et al.Recommender system using item based collaborative filtering and K-means[J].International Journal of Knowledge-Based and Intelligent Engineering Systems,2019,23(2):93-101. [14]FORSATI R,DOUSTDAR H M,SHAMSFARD M,et al.Afuzzy co-clustering approach for hybrid recommender systems[J].International Journal of Hybrid Intelligent Systems,2013,10(2):71-81. [15]BILGE A,POLAT H.A comparison of clustering-based privacy-preserving collaborative filtering schemes[J].Applied Soft Computing,2013,13(5):2478-2489. [16]TSAI C,HUNG C.Cluster ensembles in collaborative filteringrecommendation[J].Applied Soft Computing,2012,12(4):1417-1425. [17]MU J S,JING X J,HUANG H,et al.Subspace-Based Method for Spectrum Sensing With Multiple Users Over Fading Channel[J].IEEE Communications Letters,2018,22(4):848-851. [18]CHANG Y,CHEN J R,TSAI Y C.Mining Subspace Clusters from DNA Microarray Data Using Large Itemset Techniques[J].Journal of Computational Biology,2009,16(5):745-768. [19]HOULE M E,MA X G,ORIA V,et al.Efficient similaritysearch within user-specified projective subspaces[J].Information Systems,2016,59:2-14. [20]AGARWAL N,HAQUE E,LIU H,et al.Research Paper Re-commender Systems:A Subspace Clustering Approach[C]//Advances in Web-Age Information Management 6th International Conference.Hangzhou:Springer,2005:475-491. [21]RAMEZANI M,MORADI P,AKHLAGHIAN F.A patternmining approach to enhance the accuracy of collaborative filtering in sparse data domains[J].Physica A:Statistical Mechanics and its Applications,2014,408(32):72-84. [22]KIM Y A,AHMAD M.Trust,distrust and lack of confidence of users in online social media-sharing communities[J].Knowledge Based Systems,2013,37:438-450. [23]HAMIDREZA K,KOUROSH K.A new method to find neighbor users that improves the performance of Collaborative Filtering[J].Expert Systems with Applications,2017,83:30-39. [24]LI Z,ZHANG L.Fast neighbor user searching for neighborhood-based collaborative filtering with hybrid user similarity measures[J].Soft Computing,2021,25(7):5323-5338. [25]HARPER F M,KONSTAN J A.The MovieLens Datasets:History and Context[J].ACM Transactions on Interactive Intelligent Systems,2016,5(4):1-19. [26]GUO G B,ZHANG J,YORKESMITH N.A Novel Bayesian Similarity Measure forRecommender Systems[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.Beijing:IJCAI/AAAI,2013:2619-2625. [27]KOREN Y,BELL R M,VOLINSKY C.Matrix Factorization Techniques for Recommender Systems[J].Computer,2009,42(8):30-37. [28]PATRA B K,LAUNONEN R,OLLIKAINEN V,et al.A:new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J].Knowledge Based Systems,2015,82:163-177. [29]ZHANG J,PENG Q,SUN S,et al.Collaborative filteringrecommendation algorithm based on user preference derived from item domain features[J].Physica A Statistical Mechanics and its Applications,2014,396(2):66-76. [30]PIRASTEH P,HWANG D,JUNG J E.Weighted SimilaritySchemes for High Scalability in User-Based Collaborative Filtering[J].Mobile Networks and Applications,2015,20(4):497-507. [31]LIU H F,HU Z,MIAN A U,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge Based Systems,2014,56:156-166. [32]GAZDAR A,HIDRI L.A new similarity measure for collaborative filtering based recommender systems[J/OL].Knowledge Based Systems,2020,188.https://www.researchgate.net/publication/335988033_A_new_Similarity_Measure_for_Collaborative_Filtering_based_Recommender_Systems. |
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