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数据分析与知识发现  2018, Vol. 2 Issue (6): 102-109     https://doi.org/10.11925/infotech.2096-3467.2018.0017
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于灰色关联分析和时间因素的协同过滤算法*
王道平, 蒋中杨(), 张博卿
北京科技大学东凌经济管理学院 北京 100083
Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor
Wang Daoping, Jiang Zhongyang(), Zhang Boqing
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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摘要 

目的】针对传统协同过滤算法中存在的相似度可分辨性低和未考虑用户兴趣漂移的问题, 本文提出一种基于灰色关联分析和时间因素的协同过滤算法以提高推荐算法的精度。【方法】首先给出基于灰色关联度的用户相似度计算方法, 其次引入时间权重函数改进Pearson相关系数相似度, 并结合两种相似度计算方法形成混合相似度, 据此选取目标用户的近邻并做出推荐, 最后采用MovieLens数据集进行测试。【结果】与传统的协同过滤算法、单独考虑灰色关联分析或时间因素的协同过滤算法相比, 本文算法的平均绝对误差降低了29.8%。【局限】本文算法时间复杂性比较高, 计算混合相似度耗时较长。【结论】混合相似度的提出, 提高了为目标用户推荐物品的准确度, 具有较高的商业化推广前景。

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王道平
蒋中杨
张博卿
关键词 灰色关联分析时间因素协同过滤混合相似度    
Abstract

[Objective] This paper presents a collaborative filtering algorithm based on gray correlation analysis and time factor, aiming to address the low similarity resolvability and user’s interest drifting issues of the traditional algorithms. [Methods] First, we proposed a new method to calculate user similarity based on gray relational degree. Then, we used the time weight function to improve the Pearson correlation coefficients. Third, we created a hybrid similarity calculation method and made recommendation based on the neighbors of the target user. Finally, we used the MovieLens dataset to examine the new algorithm. [Results] Compared with the traditional collaborative filtering algorithms and those considering gray correlation analysis or time factor alone, the proposed algorithm reduced the mean absolute error (MAE). [Limitations] It takes the proposed algorithm longer time to calculate the hybrid similarity. [Conclusions] The hybrid similarity method improves the accuracy of recommended items for the target users and has a very good commercial promotion prospect.

Key wordsGray Correlation Analysis    Time Factor    Collaborative Filtering    Hybrid Similarity
收稿日期: 2018-01-04      出版日期: 2018-07-11
ZTFLH:  F270 G35  
基金资助:*本文系国家自然科学基金项目“敏捷供应链知识服务网络的形成、演化和治理机制研究”(项目编号: 71172169)的研究成果之一
引用本文:   
王道平, 蒋中杨, 张博卿. 基于灰色关联分析和时间因素的协同过滤算法*[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor. Data Analysis and Knowledge Discovery, 2018, 2(6): 102-109.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0017      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I6/102
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1n(t) 0 1n(2) …… 1n(n)
  绝对差值
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  关联系数
  分辨系数ρ对于推荐误差的影响
  调节参数α对于推荐误差的影响
  4种算法在不同近邻数目下推荐误差的比较
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