This work explores non-negative matrix factorization based on regularized Poisson models for recommender systems with implicit-feedback data. The properties of Poisson likelihood allow a shortcut for very fast computation and optimization over elements with zero-value when the latent-factor matrices are non-negative, making it a more suitable approach than squared loss for very sparse inputs such as implicit-feedback data. A simple and embarrassingly parallel optimization approach based on proximal gradients is presented, which in large datasets converges 2-3 orders of magnitude faster than its Bayesian counterpart (Hierarchical Poisson Factorization) fit through variational inference techniques, and 1 order of magnitude faster than implicit-ALS fit with the Conjugate Gradient method.
翻译:这项工作探索了非负矩阵因子化,其基础是常规化的建议系统隐含反馈数据Poisson模型。 Poisson 的特性允许在潜在因子矩阵为非负因子矩阵时,对零值元素进行非常快速的计算和优化的捷径,使潜因子矩阵比隐含反馈数据等非常稀少的投入的平方损失更为合适。 介绍了一种基于近似梯度的简单和令人尴尬的平行优化方法,在大型数据集中,该方法比巴伊西亚对应数据组(Histranicic Poisson因子化)的2-3级星级要快,适合通过变式推断技术进行计算和优化,而比隐含因子梯度法更快的1级比隐含的ALS级要快。