论文标题
序数非负基质分解以供推荐
Ordinal Non-negative Matrix Factorization for Recommendation
论文作者
论文摘要
我们引入了一种新的非负矩阵分解（NMF）方法，以称为ORDNMF。序数数据是分类数据，在类别之间表现出自然排序。特别是，可以在推荐系统中找到它们，无论是具有明确的数据（例如评分）还是隐式数据（例如量化的播放计数）。 ORDNMF是一种概率潜在因子模型，它概括了应用于二进制数据的Bernoulli-Poisson分解（BEPOF）和泊松分解（PF）。与这些方法相反，ORDNMF绕过二进制，并可以利用对数据的更有信息表示。我们根据合适的模型增强设计有效的变分算法，并与变异PF相关。特别是，我们的算法保留了PF的可扩展性，可以应用于巨大的稀疏数据集。我们报告了有关显式和隐式数据集的建议实验，并表明ORDNMF优于BEPOF和PF应用于二进制数据。
We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the categories. In particular, they can be found in recommender systems, either with explicit data (such as ratings) or implicit data (such as quantized play counts). OrdNMF is a probabilistic latent factor model that generalizes Bernoulli-Poisson factorization (BePoF) and Poisson factorization (PF) applied to binarized data. Contrary to these methods, OrdNMF circumvents binarization and can exploit a more informative representation of the data. We design an efficient variational algorithm based on a suitable model augmentation and related to variational PF. In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets. We report recommendation experiments on explicit and implicit datasets, and show that OrdNMF outperforms BePoF and PF applied to binarized data.
