Computer Science ›› 2018, Vol. 45 ›› Issue (7): 16-21.doi: 10.11896/j.issn.1002-137X.2018.07.003

• CCF Big Data 2017 • Previous Articles     Next Articles

Collaborative Filtering Recommendation Algorithm Based on Space Transformation

ZHAO Xing-wang,LIANG Ji-ye,GUO Lan-jie   

  1. School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;
    Key Laboratory of Computational Intelligence and Chinese Information ProcessingShanxi University, Ministry of Education,Taiyuan 030006,China
  • Received:2017-07-16 Online:2018-07-30 Published:2018-07-30

Abstract: In real applications,traditional collaborative filtering recommendation algorithms are usually faced with the problem of computational scalability.To solve this problem,in the framework of item-based collaborative filtering re-commendation,a collaborative filtering recommendation algorithm based on space transformation was proposed in this paper.Concretely speaking,according to the user social network information,the users are firstly divided into different clusters by using the community discovery algorithm.Then,item clusters are found according to the corresponding relationship between users and items in the rating information matrix.And the membership of each item for each item clusters is calculated.The sparse high dimensional rating information matrix is transformed into a low dimensional dense membership matrix,and then the similarities between items are carried on the transformed matrix.The proposed algorithm was compared with other algorithms on the public data set.The experimental results show that the proposed algorithm can significantly improve the computational efficiency while guaranteeing the accuracy of recommendation.

Key words: Collaborative filtering, Scalability, Social network, Space transformation

CLC Number: 

  • TP391
[1]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.
[2]ZHU Y Y,SUN J.Recommender system:Up to now [J].Journal of Frontiers of Computer Science and Technology,2015,9(5):513-525.(in Chinese)
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]LENG Y J,LU Q,LIANG C Y.Survey of Recommendation Based on Collaborative Filtering [J].Pattern Recognition and Artificial Intelligence,2014,27(8):720-734.(in Chinese)
冷亚军,陆青,梁昌勇.协同过滤推荐技术综述[J].模式识别与人工智能,2014,27(8):720-734.
[4]GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry [J].Communications of the ACM,1992,35(12):61-70.
[5]SHI Y,LARSON M,HANJALIC A.Collaborative filtering beyond the user-item matrix:A survey of the state of the art and future challenges[J].ACM Computing Surveys,2014,47(1):1-45.
[6]LENG Y J,LIANG C Y,DING Y,et al.Method of neighborhood formation in collaborative filtering[J].Pattern Recognition and Artificial Intelligence,2013,26(10):968-974.(in Chinese)
冷亚军,梁昌勇,丁勇,等.协同过滤中一种有效的最近邻选择方法[J].模式识别与人工智能,2013,26(10):968-974.
[7]WANG J,VRIES A P D,REINDERS M J T.Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]∥29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2006.
[8]LIANG C,LENG Y.Collaborative filtering based on information-theoretic co-clustering[J].International Journal of Systems Science,2014,45(3):589-597.
[9]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].IEEE Computer,2009,42(8):30-37.
[10]ZENG W,ZENG A,LIU H,et al.Uncovering the information core in recommender systems[J].Scientific Reports,2014,4:6140.
[11]CAI Y,LEUNG H,LI Q,et al.Typicality-based collabora-tive filtering recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2010,2(3):97-104.
[12]XU B,BU J,CHEN C,et al.An exploration of improving colla-borative recommender systems via user-item subgroups[C]∥International Conference on World Wide Web.2012:21-30.
[13]LIU F,HONG J H.Use of social network information to enhancecollaborative filtering performance[J].Expert Systems with Applications,2010,37(7):4772-4778.
[14]GUO G B,ZHANG J,THALMANN D.Merging trust in colla-borative filtering to alleviate data sparsity and cold start[J].Knowledge-Based Systems,2014,57(2):57-68.
[15]GUO L J,LIANG J Y,ZHAO X W.Collaborative filtering re-commendation algorithm incorporating social network information[J].Pattern Recognition and Artificial Intelligence,2016,29(3):281-288.(in Chinese)
郭兰杰,梁吉业,赵兴旺.融合社交网络信息的协同过滤推荐算法[J].模式识别与人工智能,2016,29(3):281-288.
[16]WALTMAN L,ECK N J V.A smart local moving algorithm for large-scale modularity-based community detection[J].European Physical Journal B,2013,86(11):1-14.
[17]TANG J,HU X,LIU H.Social recommendation:A review[J].Social Network Analysis & Mining,2013,3(4):1113-1133.
[18]BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥14th Conference on Uncertainty in Artificial Intelligence.1998:43-52.
[19]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems,2014,22(1):143-177.
[18]MCCLAIN J O,RAO V R.CLUSTISZ:A Program to Test for the Quality of Clustering of a Set of Objects.Journal of Marketing Research,1975,12(4):456-460.
[19]DAVIES D L,BOULDIN D W.A cluster separation measure.IEEE Transactions on Pattern Analysis & Machine Intelligence,1979,PAMI-1(2):224-227.
[20]INCORPORATED C S I.SAS - C Socket Library for TCP-IP,Release 5.01:SAS Technical Report C-111.SAS Publishing,1992.
[21]ROUSSEEUW P.Silhouettes:A graphical aid to the interpretation and validation of cluster analysis.Journal of Computational & Applied Mathematics,1987,20(20):53-65.
[22]KRZANOWSKI W J,LAI Y T.A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering.Biometrics,1988,44(1):23-34.
[23]XIE X L,BENI G.A Validity Measure for Fuzzy Clustering.IEEE Transactions on Pattern Analysis & Machine Intelligence,1991,13(13):841-847.
[24]HALKIDI M,VAZIRGIANNIS M,BATISTAKIS Y.QualityScheme Assessment in the Clustering Process∥Principles of Data Mining and Knowledge Discovery.Springer Berlin Heidelberg,2000:265-276.
[25]HALKIDI M,VAZIRGIANNIS M.Clustering validity assessment:finding the optimal partitioning ofa data set∥IEEE International Conference on Data Mining.IEEE,2001:187-194.
[26]AMORIM R C D,HENNIG C.Recovering the number of clusters in data sets with noise features using feature rescaling factors.Information Science,2015,324:126-145.
[27]CAMPO D N,STEGMAYER G,MILONE D H.A new index for clustering validation with overlapped clusters.Expert Systems with Applications,2016,64(C):549-556.
[28]FRIEDMAN H P,RUBIN J.On Some Invariant Criteria forGrouping Data.Publications of the American Statistical Association,1967,62(320):1159-1178.
[29]SCOTT A J,SYMONS M J.Clustering Methods Based on Likelihood Ratio Criteria.Biometrics,1971,27(2):387-397.
[30]HUBERT L J,LEVIN J R.A general statistical framework for assessing categorical clustering in free recall.Psychological Bulletin,1975,83(6):1072-1080.
[31]MILLIGAN G W.An examination of the effect of six types of error perturbation on fifteen clustering algorithms.Psychometrika,1980,45(3):325-342.
[32]JAIN A K,MURTY M N,FLYNN P J.Data clustering:a review.Acm Computing Surveys,1999,31(3):264-323.
[33]XU R,WUNSCH I D.Survey of clustering algorithms.IEEE Transactions on Neural Networks,2005,16(3):645-678.
[34]LAROSE D T.Introduction to Data Mining.Boston:China Machine Press,2010.
[35]SALTON G,HARMAN D.Information retrieval.Chichester:John Wiley and Sons Ltd.,2003.
[36]MANNING C D,RAGHAVAN P,SCH TZE H.An Introduction to Information Retrieval.Journal of the American Society for Information Science & Technology,2008,61(4):852-853.
[37]WITTEN D M,TIBSHIRANI R.A framework for feature selection in clustering.Publications of the American Statistical Association,2010,105(490):713-726.
[38]SUN W,WANG J,FANG Y.Regularized k-means clustering of high-dimensional data and its asymptotic consistency.Electronic Journal of Statistics,2012,6(2):148-167.
[1] CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13.
[2] WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54.
[3] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[4] SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56.
[5] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[6] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[7] GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164.
[8] WEI Peng, MA Yu-liang, YUAN Ye, WU An-biao. Study on Temporal Influence Maximization Driven by User Behavior [J]. Computer Science, 2022, 49(6): 119-126.
[9] YU Ai-xin, FENG Xiu-fang, SUN Jing-yu. Social Trust Recommendation Algorithm Combining Item Similarity [J]. Computer Science, 2022, 49(5): 144-151.
[10] CHANG Ya-wen, YANG Bo, GAO Yue-lin, HUANG Jing-yun. Modeling and Analysis of WeChat Official Account Information Dissemination Based on SEIR [J]. Computer Science, 2022, 49(4): 56-66.
[11] ZUO Yuan-lin, GONG Yue-jiao, CHEN Wei-neng. Budget-aware Influence Maximization in Social Networks [J]. Computer Science, 2022, 49(4): 100-109.
[12] GUO Lei, MA Ting-huai. Friend Closeness Based User Matching [J]. Computer Science, 2022, 49(3): 113-120.
[13] SHAO Yu, CHEN Ling, LIU Wei. Maximum Likelihood-based Method for Locating Source of Negative Influence Spreading Under Independent Cascade Model [J]. Computer Science, 2022, 49(2): 204-215.
[14] DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[15] WANG Jian, WANG Yu-cui, HUANG Meng-jie. False Information in Social Networks:Definition,Detection and Control [J]. Computer Science, 2021, 48(8): 263-277.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!