Please wait a minute...
Advanced Search
数据分析与知识发现  2019, Vol. 3 Issue (8): 77-87     https://doi.org/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
全文: PDF (1025 KB)   HTML ( 8
输出: BibTeX | EndNote (RIS)      
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。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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      出版日期: 2019-09-29
ZTFLH:  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, 2019, 3(8): 77-87.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1015      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I8/77
  技术路线
  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
  FilmTrust和MovieLens数据集的统计信息
  基于ISA联合聚类的组推荐处理流程
  推荐算法各阶段重复实验次数分布
  FilmTrust数据集下不同ISA阈值下划分的群组个数
  MovieLens数据集下不同ISA阈值下划分的群组个数
  FilmTrust下不同阈值下ISA联合聚类的群组组内相似度分布
  MovieLens下不同阈值下ISA联合聚类的群组组内相似度分布
  FilmTrust数据集上ISA阈值、近邻取值k对MAE的影响
  MovieLens数据集上ISA阈值、近邻取值k对RMSE的影响
  FilmTrust数据集上各算法的MAE对比效果
  MovieLens数据集上各算法的RMSE对比效果
[1] 朱成纯, 张谧 . 基于活动的社交网络中的群组推荐算法设计[J]. 计算机系统应用, 2017,26(9):103-108.
[1] ( Zhu Chengchun, Zhang Mi . Predicting User Preferences for Groups in Event-Based Social Networks[J]. Computer Systems & Applications, 2017,26(9):103-108.)
[2] Zheng N, Li Q D, Liao S C, et a1. Flickr Group Recommendation Based on Tensor Decomposition [C]// Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010: 737-738.
[3] 原福永, 马琳, 梁顺攀 . 融合用户相似度和信任传播重组信任矩阵算法[J]. 燕山大学学报, 2015,39(6):535-540.
[3] ( Yuan Fuyong, Ma Lin, Liang Shunpan . Algorithm of Reconstructing Trust Matrix by Integrating User Similarity and Trust Propagation[J]. Journal of Yanshan University, 2015,39(6):535-540.)
[4] 刘宇, 吴斌, 曾雪琳 , 等. 一种基于社交网络社区的组推荐框架[J]. 电子与信息学报, 2016,38(9):2150-2157.
doi: 10.11999/JEIT160544
[4] ( Liu Yu, Wu Bin, Zeng Xuelin , et al. A Group Recommendation Framework Based on Social Network Community[J]. Journal of Electronics & Information Technology, 2016,38(9):2150-2157.)
doi: 10.11999/JEIT160544
[5] Bergmann S, Ihmels J, Barkai N . Iterative Signature Algorithm for the Analysis of Large-Scale Gene Expression Data[J]. Physical Review E, 2003,67(3):031902.
[6] 李聪 . 电子商务协同过滤可扩展性研究综述[J]. 现代图书情报技术, 2010(11):37-41.
[6] ( Li Cong . Review of Scalability Problem in E-commercee Collaborative Filtering[J]. New Technology of Library and Information Service, 2010(11):37-41.)
[7] Sarwar B M, Riedl J, Konstan J, et al. Recommender Systems for Large-Scale E-commerce: Scalable Neighborhood Formation Using Clustering [C]// Proceedings of the 5th International Conference on Computer and Information Technology. 2002.
[8] Lu J, Shambour Q, Xu Y , et al. A Web-Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques[J]. Computational Intelligence, 2013,29(1):37-69.
[9] Ortega F, Hernando A, Bobadilla J , et al. Recommending Items to Group of Users Using Matrix Factorization Based Collaborative Filtering[J]. Information Sciences, 2016,345:313-324.
[10] 黄国言, 李有超, 高建培 , 等. 基于项目属性的用户聚类协同过滤推荐算法[J]. 计算机工程与设计, 2010,31(5):1038-1041.
[10] ( Huang Guoyan, Li Youchao, Gao Jianpei , et al. Collaborative Filtering Recommendation Algorithm Based on User Clustering of Item Attributes[J]. Computer Engineering and Design, 2010,31(5):1038-1041.)
[11] 陈克寒, 韩盼盼, 吴健 . 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013,36(2):349-359.
[11] ( Chen Kehan, Han Panpan, Wu Jian . User Clustering Based Social Network Recommendation[J]. Chinese Journal of Computers, 2013,36(2):349-359.)
[12] 王晓军 . 推荐系统中分布式混合协同过滤方法[J]. 北京邮电大学学报, 2016,39(2):25-29.
[12] ( Wang Xiaojun . A Distributed Hybrid Collaborative Filtering Method in Recommender Systems[J]. Journal of Beijing University of Posts and Telecommunications, 2016,39(2):25-29.)
[13] 黄贤英, 李沁东, 熊李媛 . 结合拓扑势用户聚类的协同过滤推荐算法[J]. 计算机工程与设计, 2018,39(1):90-95.
[13] ( Huang Xianying, Li Qindong, Xiong Liyuan . Collaborative Filtering Recommendation Algorithm with Topology Potential Combined User Clustering[J]. Computer Engineering and Design, 2018,39(1):90-95.)
[14] 王兴茂, 张兴明, 吴毅涛 , 等. 基于启发式聚类模型和类别相似度的协同过滤推荐算法[J]. 电子学报, 2016,44(7):1708-1713.
doi: 10.3969/j.issn.0372-2112.2016.07.027
[14] ( Wang Xingmao, Zhang Xingming, Wu Yitao , et al. A Collaborative Recommendation Algorithm Based on Heuristic Clustering Model and Category Similarity[J]. Acta Electronica Sinica, 2016,44(7):1708-1713.)
doi: 10.3969/j.issn.0372-2112.2016.07.027
[15] 张峻玮, 杨洲 . 一种基于改进的层次聚类的协同过滤用户推荐算法研究[J]. 计算机科学, 2014,41(12):176-178.
[15] ( Zhang Junwei, Yang Zhou . Collaborative Filtering Recommendation Algorithm Based on Improved User Clustering[J]. Computer Science, 2014,41(12):176-178.)
[16] 李华, 张宇, 孙俊华 . 基于用户模糊聚类的协同过滤推荐研究[J]. 计算机科学, 2012,39(12):83-86.
[16] ( Li Hua, Zhang Yu, Sun Junhua . Research on Collaborative Filtering Recommendation Based on User Fuzzy Clustering[J]. Computer Science, 2012,39(12):83-86.)
[17] 李贵, 陈召新, 李征宇 , 等. 基于谱聚类群组发现的协同过滤推荐算法[J]. 计算机科学, 2014,41(11A):354-358.
[17] ( Li Gui, Chen Zhaoxin, Li Zhengyu , et al. Collaborative Filtering Recommendation Algorithm Based on Spectral Clustering Subgroups Discovering[J]. Computer Science, 2014,41(11A):354-358.)
[18] Zheng N, Bao H. Flickr Group Recommendation Based on User-Generated Tags and Social Relations via Topic Model [C]// Proceedings of the 10th International Symposium on Neural Networks. Springer, 2013: 514-523.
[19] 陈婷, 朱青, 周梦溪 , 等. 社交网络环境下基于信任的推荐算法[J]. 软件学报, 2017,28(3):721-731.
[19] ( Chen Ting, Zhu Qing, Zhou Mengxi , et al. Trust-Based Recommendation Algorithm in Social Network[J]. Journal of Software, 2017,28(3):721-731.)
[20] Quijanosánchez L, Reciogarcía J A, Díazagudo B. Personality and Social Trust in Group Recommendations [C]// Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence. IEEE, 2010,2:121-126.
[21] Lai C H, Liu D R, Lin C S . Novel Personal and Group-Based Trust Models in Collaborative Filtering for Document Recommendation[J]. Information Sciences, 2013,239:31-49.
[22] Kagita V R, Pujari A K, Padmanabhan V . Virtual User Approach for Group Recommender Systems Using Precedence Relations[J]. Information Sciences, 2015,294:15-30.
[23] Hartigan J A . Direct Clustering of a Data Matrix[J]. Journal of the American Statistical Association, 1972,67(337):123-129.
[24] Busygin S, Prokopyev O, Pardalos P M . Biclustering in Data Mining[J]. Computers & Operations Research, 2008,35(9):2964-2987.
[25] Dhillon I S. Co-clustering Documents and Words Using Bipartite Spectral Graph Partitioning [C]// Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2001: 269-274.
[26] Rege M, Dong M, Fotouhi F. Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning [C]// Proceedings of the 6th International Conference on Data Mining. IEEE, 2006: 532-541.
[27] Bichot C E . Co-clustering Documents and Words by Minimizing the Normalized Cut Objective Function[J]. Journal of Mathematical Modelling & Algorithms, 2010,9(2):131-147.
[28] Castro P A D D, Franca F O D, Ferreira H M, et al. Applying Biclustering to Perform Collaborative Filtering [C]// Proceedings of the 7th International Conference on Intelligent Systems Design and Applications. IEEE, 2007: 421-426.
[29] Pera M S, Ng Y K . A Group Recommender for Movies Based on Content Similarity and Popularity[J]. Information Processing & Management, 2013,49(3):673-687.
[30] Alqadah F, Reddy C K, Hu J , et al. Biclustering Neighborhood-Based Collaborative Filtering Method for Top-N Recommender Systems[J]. Knowledge & Information Systems, 2015,44(2):475-491.
[31] Chandralekha M, Saranya K G, Sudha G . Biclustering Based Collaborative Filtering Algorithm for Personalized Web Service Recommendation[J]. International Journal of Computer Applications, 2016,142(7):18-24.
[32] 吴湖, 王永吉, 王哲 , 等. 两阶段联合聚类协同过滤算法[J]. 软件学报, 2010,21(5):1042-1054.
[32] ( Wu Hu, Wang Yongji, Wang Zhe , et al. Two-Phase Collaborative Filtering Algorithm Based on Co-Clustering[J]. Journal of Software, 2010,21(5):1042-1054.)
[33] 袁慧, 刘宏宇 . 基于群体兴趣的联合协同过滤算法[J]. 软件学报, 2004,15(1):593-601.
[33] ( Yuan Hui, Liu Hongyu . Group Interest Based Collaborative Filtering Algorithm[J]. Journal of Software, 2004,15(1):593-601.)
[34] 王玙, 刘东苏 . 基于联合聚类与用户特征提取的协同过滤推荐算法[J]. 情报学报, 2017,36(8):852-858.
[34] ( Wang Yu, Liu Dongsu . Collaborative Filtering Algorithm Based on Bi-clustering and User Attribution Extraction[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(8):852-858.)
[35] Gartrell M, Xing X, Lv Q, et al. Enhancing Group Recommendation by Incorporating Social Relationship Interactions [C]// Proceedings of the 16th ACM International Conference on Supporting Group Work. DBLP, 2010: 97-106.
[36] Ghazanfar M A, Prügel-Bennett A . Leveraging Clustering Approaches to Solve the Gray-Sheep Users Problem in Recommender Systems[J]. Expert Systems with Applications, 2014,41(7):3261-3275.
[37] Shi J, Wu B, Lin X. A Latent Group Model for Group Recommendation [C]// Proceedings of the 2015 IEEE International Conference on Mobile Services. IEEE, 2015: 233-238.
[1] 李振宇, 李树青. 嵌入隐式相似群的深度协同过滤算法*[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[2] 杨辰, 陈晓虹, 王楚涵, 刘婷婷. 基于用户细粒度属性偏好聚类的推荐策略*[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[3] 杨恒,王思丽,祝忠明,刘巍,王楠. 基于并行协同过滤算法的领域知识推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[4] 苏庆,陈思兆,吴伟民,李小妹,黄佃宽. 基于学习情况协同过滤算法的个性化学习推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[5] 郑淞尹,谈国新,史中超. 基于分段用户群与时间上下文的旅游景点推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[6] 熊回香,李晓敏,李跃艳. 基于图书评论属性挖掘的群组推荐研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 214-222.
[7] 张纯金,郭盛辉,纪淑娟,杨伟,伊磊. 基于多属性评分隐表征学习的群组推荐算法*[J]. 数据分析与知识发现, 2020, 4(12): 120-135.
[8] 丁勇,陈夕,蒋翠清,王钊. 一种融合网络表示学习与XGBoost的评分预测模型*[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
[9] 焦富森,李树青. 基于物品质量和用户评分修正的协同过滤推荐算法 *[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[10] 李杰, 杨芳, 徐晨曦. 考虑时间动态性和序列模式的个性化推荐算法*[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[11] 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.
[12] 王永, 王永东, 郭慧芳, 周玉敏. 一种基于离散增量的项目相似性度量方法*[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[13] 花凌锋, 杨高明, 王修君. 面向位置的多样性兴趣新闻推荐研究*[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[14] 薛福亮, 刘君玲. 基于用户间信任关系改进的协同过滤推荐方法*[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[15] 覃幸新, 王荣波, 黄孝喜, 谌志群. 基于多权值的Slope One协同过滤算法*[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn