Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.

翻译：矩阵分解模型是当前商业协同过滤推荐系统的核心。本文测试了六种具有代表性的矩阵分解模型，使用了四个协同过滤数据集。实验评估了多种准确性及超越准确性的质量指标，包括预测、有序与无序列表推荐、新颖性和多样性。结果表明，每种便捷的矩阵分解模型在以下方面各有侧重：其简洁性、所需的预测质量、必要的推荐质量、期望的推荐新颖性与多样性、解释推荐的需求、为隐式因子赋予语义解释的适当性、向用户群体推荐的可行性，以及获取可靠性值的必要性。为确保实验的可复现性，本研究采用了一个开放框架，并提供了实现代码。