基于偏置度量分解与隐反馈的协同过滤推荐算法

矩阵分解由于其简单可靠的特性，是推荐系统中最重要的算法之一，由于内积无法完全捕捉用户和商品间的交互，矩阵分解的性能难以继续提升。为了解决这个问题，改进了基础的距离度量分解模型，提出了基于偏置度量分解与隐反馈的协同过滤推荐算法，并对用户评分时间动态建模，进一步提升了模型性能。针对推荐系统中最常见的评分预测任务，分别在三个数据集上进行实验验证，实验结果表明所提出的模型的预测准确率有明显提升。

Collaborative filtering recommendation algorithm based on biased metric factorization and implicit feedback

Matrix factorization is one of the most important algorithms in recommendation systems due to its simple and reliable characteristics. However, since the inner product can''t fully capture the interaction between users and items, it is difficult to further enhance the performance of matrix factorization. In order to solve this problem, this paper modified the basic distance metric factorization model and proposed a collaborative filtering recommendation algorithm based on biased metric factorization and implicit feedback, and dynamically modeled users'' rating timestamps, which further improved the model performance. This paper tested the most common rating prediction task in the recommendation system on three data sets. The experimental results show that the prediction accuracy of the proposed model is significantly improved.