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数据分析与知识发现  2019, Vol. 3 Issue (4): 117-125    DOI: 10.11925/infotech.2096-3467.2018.0662
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于条件型游走的四部图推荐方法*
张怡文1(),张臣坤1,杨安桔1,计成睿1,岳丽华2
1安徽新华学院信息工程学院 合肥 230088
2中国科学技术大学计算机学院 合肥 230026
A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm
Yiwen Zhang1(),Chenkun Zhang1,Anju Yang1,Chengrui Ji1,Lihua Yue2
1Institute of Information Engineering, Anhui Xinhua University, Hefei 230088, China
2School of Computer, University of Science and Technology of China, Hefei 230026, China
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摘要 

【目的】通过挖掘用户与项目、用户与类别的关系特征, 提取用户偏好, 优化个性化推荐效果。【方法】提取用户对项目的评分和项目的度属性, 挖掘用户偏好, 提出用户-项目二部图上的游走条件; 通过用户-项目-类别三部图映射到用户-类别二部图, 构建类别-用户-项目-类别四部图; 建立通过项目和类别共同挖掘用户偏好的个性化推荐方法。【结果】利用MovieLens电影评分数据, 分别对基于二部图、加权二部图、三部图的方法与本文方法进行对比实验, 结果表明, 本文方法在准确率、MAE、召回率、覆盖率方面分别有所优化。【局限】MovieLens数据集缺少用户对电影评论性的文字数据集, 不能通过语义分析用户偏好。【结论】本文对用户评分和项目度属性进行用户偏好分析, 通过条件型游走四部图推荐方法, 优化推荐效果。

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张怡文
张臣坤
杨安桔
计成睿
岳丽华
关键词 推荐系统四部图条件游走个性化推荐    
Abstract

[Objective] By mining the relation characteristics between users and items, or between users and categories, this Paper extracts user preferences to optimize recommendation effect. [Methods] This paper extracts user rating and items degree attribute, mines user preferences, and puts forward the walk condition of User-Item bipartite graph; The category-User-Project-Category quadripartite graph is established by mapping User-Item-Category tripartite graph to the User-Category bipartite graph. The personalized recommendation method for user preferences through items and categories is proposed. [Results] Choosing MovieLens ratings data set as the source data, respectively comparing the experimental difference based on bipartite graph, weighted bipartite graph, tripartite graph and quadripartite graph, the results show that the Precision rate, MAE, recall rate, and coverage have been respectively optimized with this proposed method. [Limitations] Due to Movielens lack of critical textual data of users for movies, it is hard to analyze user preferences through the semantic. [Conclusions] This research analyzed user preferences through user ratings and degree attribute, it can be determined that the recommendation effect of quadripartite graph based on conditional walk is great.

Key wordsRecommendation System    Quadripartite Graph    Conditional Walk    Personalized Recommendation
收稿日期: 2018-06-21     
基金资助:*本文系安徽省高校优秀青年人才支持计划重点项目(项目编号: gxyqZD2018087)、安徽省质量工程项目“精品资源共享课程”(项目编号: 2015gxk081)和安徽新华学院校级团队项目“基于用户兴趣的二部图随机游走推荐方法研究”(项目编号: 2016td020)的研究成果之一
引用本文:   
张怡文,张臣坤,杨安桔,计成睿,岳丽华. 基于条件型游走的四部图推荐方法*[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0662.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0662
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