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Issue 3
Sep.  2016
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WANG Wei, ZHENG Jun. Improved collaborative filtering algorithm based on usersimilarity[J]. Journal of East China Normal University (Natural Sciences), 2016, (3): 60-66. doi: 10.3969/j.issn.1000-5641.2016.03.007
Citation: WANG Wei, ZHENG Jun. Improved collaborative filtering algorithm based on usersimilarity[J]. Journal of East China Normal University (Natural Sciences), 2016, (3): 60-66. doi: 10.3969/j.issn.1000-5641.2016.03.007

Improved collaborative filtering algorithm based on usersimilarity

doi: 10.3969/j.issn.1000-5641.2016.03.007
  • Received Date: 2015-05-22
  • Publish Date: 2016-05-25
  • Collaborative filtering is widely accepted and applied currently as one of the most popular personalized recommendation methods. It is an implementation method based on content that has considerable advantages in accuracy. The core issue of collaborative filtering is how to work out the calculation of similarity. In this paper, we introduce the traditional collaborative filtering algorithm and make similarity calculation more accurately by optimizing the traditional formula of similarity. Experimental results show that the optimized algorithm can improve the accuracy of the recommendation and reduce the MAE (Mean Absolute Error, MAE) efficiently.
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  • [1]
    [1]刘建国,周涛,汪秉红.个性化推荐系统的研究发展[J].自然科学进展,2009,19(1):115.

    [2]孟祥武,纪威宇,张玉洁.大数据环境下的推荐系统[J].北京邮电大学学报,2015,38(2):115.

    [3]硕良勋,柴变芳,张新东.基于改进最近邻的协同过滤推荐算法[J].计算机工程与应用,2015,51(5): 137141.

    [4]ZHAO Z D, SHANG M S. Userbased collaborativefiltering recommendation algorithms on Hadoop[C]//Proceedings of the 2010 3rd International Conference on Knowledge Discovery and Data Mining. IEEE, 2010: 478481.

    [5]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,2003,114(9) :16211628.

    [6]彭玉,程小平,徐艺萍.一种改进的Itembased协同过滤推荐算法[J].西南大学学报(自然科学版),2007,29(5):146149.

    [7]刘芳先,宋顺林.改进的协同过滤推荐算法[J].计算机工程与应用,2011,47(8):7275.

    [8]汪静,印鉴,郑利荣,等.基于共同评分和相似性权重的协同过滤推荐算法[J].计算机科学,2010,37(2):99104.

    [9]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, 2015,17(6):734749.

    [10]孟庆庆,张胜男,卢楚雍.基于用户特征和商品特征的组合协同过滤算法[J].软件导刊,2015,14(3):4143.

    [11]孙辉,马跃,杨海波,等.一种相似度改进的用户聚类协同过滤推荐算法[J].小型微型计算机系统,2014,35(9):19671970.

    [12]傅鹤岗,彭晋.基于模范用户的改进协同过滤算法[J].计算机工程,2011,37(3):7074.

    [13]王立印,张辉,陈勇.一种基于DiceEuclidean相似度计算的协同过滤算法[J].计算机应用研究,2015,32(10):28912895.

    [14]朱毅萌,谢颖华.分步筛选邻居的协同过滤改进算法[J].计算机系统应用,2015,24(6):132137.

    [15]张丙奇.基于领域知识的个性化推荐算法研究[J].计算机工程,2005,31(21): 79.

    [16]蔡淑琴,袁乾,周鹏,等.基于信息传播理论的微博协同过滤推荐模型[J].系统工程理论与实践,2015,35(5):12671275.
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