Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (8): 77-87    DOI: 10.11925/infotech.2096-3467.2018.1015
Current Issue | Archive | Adv Search |
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
Download: PDF(1025 KB)   HTML ( 6
Export: BibTeX | EndNote (RIS)      

[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     
Received: 11 September 2018      Published: 29 September 2019
ZTFLH:  TP391 G35  
Corresponding Authors: Shan Li     E-mail:

Cite this article:

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.

URL:     OR

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
[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] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[2] Wangqiang Zhang,Zhongming Zhu,Yamei Li,Linong Lu,Wei Liu. Disambiguating Author Names Automatically for Institutional Repository[J]. 数据分析与知识发现, 2019, 3(6): 92-98.
[3] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[4] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[5] Yong Wang,Yongdong Wang,Huifang Guo,Yumin Zhou. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[6] Lingfeng Hua,Gaoming Yang,Xiujun Wang. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[7] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[8] Xingxin Qin,Rongbo Wang,Xiaoxi Huang,Zhiqun Chen. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[9] Chunlei Yang. Quantification Constraint System for Pragmatic Disambiguation: From Linguistic Design to Computational Implementation[J]. 数据分析与知识发现, 2017, 1(11): 1-11.
[10] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[11] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[12] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[13] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[14] Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity[J]. 数据分析与知识发现, 2016, 32(12): 44-49.
[15] Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. 现代图书情报技术, 2015, 31(9): 38-45.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938