Boolean Matrix Factorisation for Collaborative Filtering: An FCA-Based Approach

被引：0

作者：

Ignatov, Dmitry I. ^{[1
]}

Nenova, Elena ^{[2
]}

Konstantinova, Natalia ^{[3
]}

Konstantinov, Andrey V. ^{[1
]}

机构：

[1] Natl Res Univ, Higher Sch Econ, Moscow, Russia

[2] Imhonet, Moscow, Russia

[3] Wolverhampton Univ, Wolverhampton, W Midlands, England

来源：

ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, AND APPLICATIONS | 2014年 / 8722卷

关键词：

Boolean Matrix Factorisation; Formal Concept Analysis; Singular Value Decomposition; Recommender Algorithms; FORMAL CONCEPT ANALYSIS;

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：

We propose a new approach for Collaborative filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (MovieLens dataset) we compare the approach with an SVD-based one in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF as for the SVD-based algorithm in case of non-scaled data.

引用

页码：47 / 58

页数：12

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