引入评分偏置的二项矩阵分解推荐算法

Binomial matrix factorization with rating drift for recommender systems

针对推荐系统中的评分预测问题,在矩阵分解的基础上实现了一种修正的二项矩阵分解算法。假设用户对物品的评分基于二项分布,由于用户的评分习惯存在差异,物品的受欢迎程度也存在差异,导致用户-物品评分矩阵存在偏置量。通过引入偏置量对矩阵分解和评分预测进行修正,采用最大后验估计建模,并通过随机梯度下降算法优化模型。实验结果表明,在MovieLens 100K数据集上,引入评分偏置的二项矩阵分解算法在推荐精度、离线计算时间等方面均优于传统的二项矩阵分解算法。

Based on matrix factorization techniques,this paper implemented a modified binomial matrix decomposition algorithm in order to solve the recommender system’s rating prediction problem.It supposed that the user’s rating of the item was based on the binomial distribution.There were differences in the user’s rating habits,popularity of the items,it would result in an offset in the user-item scoring matrix.This paper used the maximum a posteriori estimate to design model and optimized the model by a stochastic gradient descent algorithm.The experimental results show that the modified binomial matrix decomposition algorithm is superior to the traditional binomial matrix decomposition algorithm in terms of recommender accuracy and offline calculation time on the MovieLens 100 K datasets.