一种基于FCA的布尔矩阵分解协同过滤算法
An FCA-based Boolean Matrix Factorisation for Collaborative Filtering
摘要：
提出了一种基于布尔矩阵分解和形式概念分析的协同过滤方法。在一系列的实验中，真实数据（Movielens数据集），我们比较的方法与SVD和NMF为基础的算法的平均误差（MAE）。实验结果之一是，它是足够的，有一个二进制缩放的评级数据，以获得几乎相同的质量方面的MAE BMF比基于SVD的算法在非缩放数据的情况下。
英文摘要：
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 the SVD- and NMF-based algorithms 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 than for the SVD-based algorithm in case of non-scaled data.
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