An Efficient Collaborative Recommender System Based on k-Separability

被引：0

作者：

Alexandridis, Georgios ^{[1
]}

Siolas, Georgios ^{[1
]}

Stafylopatis, Andreas ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Dept Elect & Comp Engn, Athens 15780, Greece

来源：

ARTIFICIAL NEURAL NETWORKS (ICANN 2010), PT III | 2010年 / 6354卷

关键词：

collaborative recommender; sparsity problem; k-separability; constructive ANN architecture;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most recommender systems usually have too many items to recommend to too many users using limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This article outlines a collaborative recommender system, that tries to amend this situation. The system is built around the notion of k-separability combined with a constructive neural network algorithm.

引用

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页码：198 / 207

页数：10

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