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数据分析与知识发现  2019, Vol. 3 Issue (8): 77-87    DOI: 10.11925/infotech.2096-3467.2018.1015
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于ISA联合聚类的组推荐算法研究 *
李珊1(),姚叶慧1,厉浩2,刘洁1,嘎玛白姆1
1南京航空航天大学经济与管理学院南京 210016
2江苏省教育考试院南京210024
ISA Biclustering Algorithm for Group Recommendation
Shan Li1(),Yehui Yao1,Hao Li2,Jie Liu1,Karmapemo1
1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2Jiangsu Provincial Education Examination Authority, Nanjing 210024, China
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摘要 

【目的】避免在组推荐群组划分阶段对群组个数k值的经验依赖, 提高推荐算法的准确率及扩展性。【方法】应用ISA联合聚类算法, 从用户、项目两个维度同时聚类, 获取精准的重叠兴趣群组; 在各群组内结合用户专业度构建出代表群组共同偏好的虚拟用户; 最后基于虚拟用户进行协同过滤推荐。【结果】通过ISA联合聚类摆脱了k值依赖, 基于ISA联合聚类的组推荐算法在FilmTrust数据集200和500群组规模的MAE值分别为0.697和0.693, MovieLens数据集上RMSE值为1.022, 与其他算法相比准确率有所提升。【局限】基于ISA的群组划分算法具有一定的随机性, 需多次重复实验。【结论】本文算法能够摆脱传统聚类算法对k值的经验依赖, 有效提高协同过滤推荐算法的准确率及扩展性。

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李珊
姚叶慧
厉浩
刘洁
嘎玛白姆
关键词 组推荐ISA联合聚类虚拟用户协同过滤协同过滤    
Abstract

[Objective]This paper tries to improve the recommendation algorithm, aiming to reduce the dependence on the number of groups (k value) at the catorization stage.[Methods]Weused the ISA algorithm to modify the collaborative filtering algorithm and finish the clustering tasks from the perspectives of users and projects. Then, we created a virtual user representing the group interests based on user’s expertise. Finally, we predicted the target users’ ratings based on the new collaborative filtering algorithm.[Results]This algorithm can remove the empirical dependence of k, and improve the accuracy of collaborative filtering recommendation algorithm. The MAE was reduced to 0.697 with 200 groups and the MAE was reduced to 0.693 with 500 groups from the FilmTrust dataset. The RMSE was reduced to 1.022 with the MovieLens dataset. [Limitations]Several rounds of repeating experience are needed to improve the quality of this study.[Conclusions] This algorithm does not rely on the dependence of k, and effectively improves the performance of collaborative filtering recommendation algorithm.

Key wordsGroup Recommendation    ISA    Bi-Clustering    Virtual User    Collaborative Filtering
收稿日期: 2018-09-11     
中图分类号:  TP391 G35  
基金资助:*本文系中央高校基本科研业务费专项资金资助项目“网络舆情中商品口碑意见领袖的识别研究”(NR2016004);江苏省教育科学十二五规划课题“基于数据中心的教育考试管理服务平台研究”(K-a/2015/01);江苏省教育科学十二五规划课题“基于手写识别和机器学习的纸笔考试智能评卷研究”的研究成果之一(K-a/2018/01)
通讯作者: 李珊     E-mail: lishan@nuaa.edu.cn
引用本文:   
李珊,姚叶慧,厉浩,刘洁,嘎玛白姆. 基于ISA联合聚类的组推荐算法研究 *[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1015.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1015
图1  技术路线
图2  联合聚类结果示意
数据集 用户数 项目数 评分数 稀疏度 用户平均度 项目平均度
FilmTrust 1 508 2 071 35 494 1.1365% 23.54 17.14
MovieLens 943 1 682 100 000 6.3046% 106.04 59.45
表1  FilmTrust和MovieLens数据集的统计信息
图3  基于ISA联合聚类的组推荐处理流程
图4  推荐算法各阶段重复实验次数分布
图5  FilmTrust数据集下不同ISA阈值下划分的群组个数
图6  MovieLens数据集下不同ISA阈值下划分的群组个数
图7  FilmTrust下不同阈值下ISA联合聚类的群组组内相似度分布
图8  MovieLens下不同阈值下ISA联合聚类的群组组内相似度分布
图9  FilmTrust数据集上ISA阈值、近邻取值k对MAE的影响
图10  MovieLens数据集上ISA阈值、近邻取值k对RMSE的影响
图11  FilmTrust数据集上各算法的MAE对比效果
图12  MovieLens数据集上各算法的RMSE对比效果
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