An enterprise-friendly book recommendation system for very sparse data

被引:0
作者
Desai, Tejash [1 ]
Gandhi, Sahil [1 ]
Murlidhar, Pranav [1 ]
Gupta, Sankalp [1 ]
Vijayalakshmi, M. [2 ]
Bhole, G. P. [3 ]
机构
[1] VJTI, Mumbai, Maharashtra, India
[2] VESIT, Mumbai, Maharashtra, India
[3] VJTI, Head Comp Engn & Informat Technol, Mumbai, Maharashtra, India
来源
2016 INTERNATIONAL CONFERENCE ON COMPUTING, ANALYTICS AND SECURITY TRENDS (CAST) | 2016年
关键词
recommender systems; biclustering; data mining; big data;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recommendation systems designed using biclustering handle the existing duality between users and items, which is not observed in other popular approaches. However, biclustering is generally limited by sparsity in the data and usually requires huge computational powers. In this paper, we propose a ready-for-enterprise book recommendation system using the biclustering algorithm. Our proposed algorithm consists of a hybrid approach containing an initial cluster phase which is taken as input for a biclustering phase. We show that our approach not only proves to be scalable dealing with large amounts of sparsity but also produces results with error values comparable to other state-of-the-art approaches, thereby making it enterprise-friendly.
引用
收藏
页码:211 / 215
页数:5
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