Abstract A latent factor analysis (LFA)-based model has outstanding performance in extracting desired patterns from High-dimensional and Sparse (HiDS) data for building a recommender systems. However, they mostly fail in acquiring non-linear features from an HiDS matrix. An AutoEncoder (AE)-based model can address this issue efficiently, but it requires filling unknown data of an HiDS matrix with pre-assumptions that leads to the following limitations: a) prefilling unknown data of an HiDS matrix might skew its known data distribution to generate ridiculous recommendations; and b) incorporating a deep AE-style structure to improve its representative learning ability. Experimental results on three HiDS matrices from real recommender systems show that an FDAE-based model significantly outperforms state-of-the-art recommenders in terms of recommendation accuracy. Meanwhile, its computational efficiency is comparable with the most efficient recommenders with the help of parallelization.

中文翻译：

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推荐系统中用于高维和稀疏矩阵的快速深度自动编码器

摘要 基于潜在因子分析 (LFA) 的模型在从高维稀疏 (HiDS) 数据中提取所需模式以构建推荐系统方面具有出色的性能。然而，它们大多无法从 HiDS 矩阵中获取非线性特征。基于 AutoEncoder (AE) 的模型可以有效地解决这个问题，但它需要使用预先假设填充 HiDS 矩阵的未知数据，这会导致以下限制：a) 预填充 HiDS 矩阵的未知数据可能会扭曲其已知数据分布产生荒谬的建议；b) 结合深度 AE 式结构以提高其代表性学习能力。来自真实推荐系统的三个 HiDS 矩阵的实验结果表明，基于 FDAE 的模型在推荐准确性方面明显优于最先进的推荐系统。同时，在并行化的帮助下，其计算效率可与最高效的推荐系统相媲美。