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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (8): 77-87    DOI: 10.11925/infotech.2096-3467.2018.1015
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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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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     
Received: 11 September 2018      Published: 29 September 2019
ZTFLH:  TP391 G35  
Corresponding Authors: Shan Li     E-mail: lishan@nuaa.edu.cn

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:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1015     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I8/77

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
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