计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 230-235.doi: 10.11896/j.issn.1002-137X.2017.08.039
何明,肖润,刘伟世,孙望
HE Ming, XIAO Run, LIU Wei-shi and SUN Wang
摘要: 协同过滤直接根据用户的行为记录去预测其可能感兴趣的项目,是现今最成功、应用最广泛的推荐技术。推荐的准确度受相似性度量方法效果的影响。传统的相似性度量方法主要关注用户共同评分项之间的相似度,忽视了评分项目中的类别信息,在面对数据稀疏性问题时存在一定的不足。针对上述问题,提出基于分类信息 的评分矩阵填充方法,结合用户兴趣相似度计算方法并充分考虑到评分项目的类别信息,使得兴趣度的度量更加符合推荐系统应用的实际情况。实验结果表明,该算法可以弥补传统相似性度量方法的不足,缓解评分数据稀疏对协同过滤算法的影响,能够提高推荐的准确性、多样性和新颖性。
[1] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-art and possible extensions[J].Transactions on Knowledge and Data Engineering,2005,7(6):734-749. [2] XU H L,WU X,LI X D,et al.Comparison study of Internet re-commendation system [J].Journal of Software,2009,0(2):350-362.(in Chinese) 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362. [3] AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51. [4] DAI J L.Study on the sparsity problem of collaborative filtering algorithm [D].Chongqing:Chongqing University,2013.(in Chinese) 代金龙.协同过滤算法中数据稀疏性问题研究[D].重庆:重庆大学,2013. [5] DENG A L,ZHU Y Y,SHI B L.A collaborative filtering re-commendation algorithm based on item ratingprediction[J].Journal of Software,2003,4(9):1621-1628.(in Chinese) 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628. [6] 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. [7] JI X S,LIU Y B,LUO L M.Similarity measurement based on interest in collaborative filtering [J].Journal of Computer Applications,2010,0(10):2618-2620.(in Chinese) 嵇晓声,刘宴兵,罗来明.协同过滤中基于用户兴趣度的相似性度量方法[J].计算机应用,2010,30(10):2618-2620. [8] CLEGER-TAMAYO S,FERNNDEZ-LUNA J M,HUETE J F.Top-N news recommendations in digital newspapers[J].Knowledge-Based Systems,2012,27(6):180-189. [9] ZHANG X S.Research on collaborative filtering recommenda-tion algorithms for data sparsity[D].Hefei:University of Science & Technology China,2011.(in Chinese) 张学胜.面向数据稀疏的协同过滤推荐算法研究[D].合肥:中国科学技术大学,2011. [10] YU X.Research on recommendation methodsbased on collaborative filtering techniques[D].Tianjin:Tianjin University,2009.(in Chinese) 郁雪.基于协同过滤技术的推荐方法研究[D].天津:天津大学,2009. [11] FAN B,CHENG J J.Collaborative filtering recommendation algorithm based on user’s multi-similarity[J].Computer Science,2012,9(1):23-26.(in Chinese) 范波,程久军.用户间多相似度协同过滤推荐算法[J].计算机科学,2012,39(1):23-26. [12] MILLER B N,ALBERT I,LAM S K,et al.MovieLens unplug-ged:experiences with an occasionally connected recommender system[C]∥Proceedings of the 8th International Conference on Intelligent User Interfaces.ACM,2003:263-266. [13] LUO X,OUYANG Y X,XIONG Z,et al.The effect of similarity support in K-Nearest-Neighborhood based collaborative filtering [J].Chinese Journal of Computers,2010,3(8):1437-1445.(in Chinese) 罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445. [14] STECK H.Evaluation of recommendations:rating-predictionand ranking[C]∥Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:213-220. [15] SHI Y,LARSON M,HANJALIC A.Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation[J].Information Sciences,2013,229(6):29-39. [16] Apache.Mahout[EB/OL].[2016-06-03].http://mahout.apache.org. [17] MovieLens datasets [EB/OL].[2016-06-16].http://group-lens.org/datasets/movielens. [18] KOREN Y,BELL R M,VOLINSKY C.Matrix factorization te-chniques for recommender systems[J].IEEE Computer,2009,42(8):30-37. |
No related articles found! |
|