Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 163-166.

• Data Science • Previous Articles     Next Articles

Research on Relationship Between Bipartite Network Recommendation Algorithm and Collaborative Filtering Algorithm

ZHOU Bo   

  1. (China Institute of Atomic Energy,Beijing 102413,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: This paper introduced the basic principle of collaborative filtering algorithm and bipartite network recommendation algorithm,and proposed the general bipartite network recommendation algorithm.The internal relationship between the two algorithms was analyzed.The results show that collaborative filtering algorithm is a special case of the bipartite network recommendation algorithm,and bipartite network algorithm is proved to perforem better than collaborative recommendation algorithm.This research systematizes and unifies the bipartite recommendation algorithm theory and promotes the further development of recommendation algorithm.

Key words: Bipartite network, Collaborative filtering, Recommendation algorithm

CLC Number: 

  • TP391
[1]裴中佑.基于随机游走的推荐技术研究及应用[D].成都:西南交通大学,2014.
[2]MASSA P,AVESANI P.Trust-Aware Collaborative Filtering for Recommender Systems[M]∥On the Move to Meaningful Internet Systems 2004:CoopIS,DOA,and ODBASE.Springer Berlin Heidelberg,2004:492-508.
[3]李聪,梁昌勇.基于n序访问解析逻辑的协同过滤冷启动消除方法[J].系统工程理论与实践,2012(7):1537-1545.
[4]李小浩.协同过滤推荐算法稀疏性与可扩展性问题研究[D].重庆:重庆大学,2015.
[5]孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].杭州:浙江大学,2005.
[6]徐键.协同过滤中数据稀疏问题与推荐实时性的研究[D].兰州:兰州大学,2016.
[7]TAO Z,JIE R,MATUˇS M,et al.Bipartite network projection and personal recommendation[J].Physical Review E,2007,76(4):70-80.
[8]周波,杨朝峰.发送者和接受者能力的二分网络推荐算法研究[J].情报工程,2016,2(2):71-80.
[9]何平凡.基于排序学习的Top-N推荐算法研究[D].北京:北京理工大学,2016.
[10]赵向宇.Top-N协同过滤推荐技术研究[D].北京:北京理工大学,2014.
[11]陈嘉颖,于炯,杨兴耀,等.基于复杂网络节点重要性的链路预测算法[J].计算机应用,2016(12):3251-3255,3268.
[12]荣莉莉,郭天柱,王建伟.复杂网络节点中心性[J].上海理工大学学报,2008,30(3):227-230.
[13]姚尊强,尚可可,许小可.加权网络的常用统计量[J].上海理工大学学报,2012,34(1):18-26.
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