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基于矩阵填充和物品可预测性的协同过滤算法

潘涛涛 文峰 刘勤让

潘涛涛, 文峰, 刘勤让. 基于矩阵填充和物品可预测性的协同过滤算法. 自动化学报, 2017, 43(9): 1597-1606. doi: 10.16383/j.aas.2017.c160644
引用本文: 潘涛涛, 文峰, 刘勤让. 基于矩阵填充和物品可预测性的协同过滤算法. 自动化学报, 2017, 43(9): 1597-1606. doi: 10.16383/j.aas.2017.c160644
PAN Tao-Tao, WEN Feng, LIU Qin-Rang. Collaborative Filtering Recommendation Algorithm Based on Rating Matrix Filling and Item Predictability. ACTA AUTOMATICA SINICA, 2017, 43(9): 1597-1606. doi: 10.16383/j.aas.2017.c160644
Citation: PAN Tao-Tao, WEN Feng, LIU Qin-Rang. Collaborative Filtering Recommendation Algorithm Based on Rating Matrix Filling and Item Predictability. ACTA AUTOMATICA SINICA, 2017, 43(9): 1597-1606. doi: 10.16383/j.aas.2017.c160644

基于矩阵填充和物品可预测性的协同过滤算法

doi: 10.16383/j.aas.2017.c160644
基金项目: 

国家自然科学基金 61572520

国家高技术研究发展计划(863计划) 2014AA01A

详细信息
    作者简介:

    文峰:文锋 江南计算技术研究所高级工程师.主要研究方向为计算机应用.E-mail:wensinliu@163.com

    刘勤让 国家数字交换系统工程技术研究中心研究员.主要研究方向为片上网络设计. E-mail: qinrangliu@sina.com

    通讯作者:

    潘涛涛 国家数字交换系统工程技术研究中心硕士生.主要研究方向为人工智能和数据挖掘.本文通信作者.E-mail: pan_taotao@126.com

Collaborative Filtering Recommendation Algorithm Based on Rating Matrix Filling and Item Predictability

Funds: 

National Natural Science Foundation of China 61572520

National High Technology Research and Development Program (863 Program) 2014AA01A

More Information
    Author Bio:

    Senior engineer at the Jiangnan Computing Technology Research Institute. His main research interest is computer application

    Researcher at the China National Digital Switching System Engineering and Technological R & D Center. His main research interest is network-on-chip

    Corresponding author: PAN Tao-Tao Master student at the China National Digital Switching System Engineering and Technological R & D Center. His research interest covers artificial intelligence and data mining. Corresponding author of this paper
  • 摘要: 针对传统矩阵填充算法忽略了预测评分与真实评分之间的可信度差异和传统Top-N方法推荐精度低等问题,提出了一种改进的协同过滤算法.该算法首先利用置信系数C区分评分值之间的可信度;然后提出物品可预测性的概念,综合物品的预测评分与物品的可预测性进行物品推荐并将其转化为0-1背包问题,从而筛选出最优化的推荐列表.实验结果表明:该算法能有效缓解稀疏性的影响,提高推荐性能,并且算法具有良好的可扩展性.
    1)  本文责任编委  周涛
  • 图  1  物品层次划分

    Fig.  1  Hierarchy of item

    图  2  $C$ 与MAE的关系

    Fig.  2  The relationship between $C$ and MAE

    图  3  $Q$ 与precision的关系

    Fig.  3  The relationship between $Q$ and precision

    图  4  Movielens_100k中 $k$ 与MAE的关系

    Fig.  4  The relationship between $k$ and MAE in Movielens_100k

    图  5  Movielens_100k中 $k$ 与precision的关系

    Fig.  5  The relationship between $k$ and precision in Movielens_100k

    图  6  Movielens_100k中 $k$ 与Coverage的关系

    Fig.  6  The relationship between $k$ and Coverage in Movielens_100k

    图  7  Movielens_100k中稀疏度与MAE的关系

    Fig.  7  The relationship between sparsity and MAE in Movielens_100k

    图  8  基于物品可预测性算法可扩展性对比

    Fig.  8  Scalability comparison of algorithms

    图  9  三种算法的运行时间对比

    Fig.  9  Comparing the running time of the three algorithms

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出版历程
  • 收稿日期:  2016-09-08
  • 录用日期:  2017-01-16
  • 刊出日期:  2017-09-20

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