对比六种矩阵分解模型在推荐系统中的表现，全面评估预测与推荐质量。
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
- 基于四个数据集测试六种主流矩阵分解模型
- 涵盖准确率、新颖性、多样性等多维度指标
- 适合关注推荐系统评估方法的研究者与工程师
矩阵分解模型是当前商业协同过滤推荐系统的核心。本文在四个协同过滤数据集上测试了六种代表性矩阵分解模型，实验涵盖了多种精度及非精度评价指标，包括预测准确性、有序与无序推荐列表的质量、新颖性与多样性。结果表明，不同模型在简单性、预测质量、推荐质量、推荐新颖性与多样性、可解释性、隐因子语义解释能力、群体推荐需求以及可靠性值生成等方面各有优劣。为确保实验可复现，研究采用开源框架，并提供完整实现代码。
原文摘要 · Abstract (English)
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.
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