基于评分可信度的大数据线性回归推荐算法

针对传统线性回归推荐算法没有考虑用户兴趣漂移、活跃度和评分可信度等影响因素,为进一步提高算法的准确度和对用户偏好的拟合度,提出一种融合评分可信度的线性回归推荐算法。首先将用户的兴趣漂移度、活跃度和用户对商品的评价信息综合考虑到用户评分可信度的计算方法中;然后将该方法融合到传统线性回归推荐算法系数矩阵求解过程当中;最后利用优化后的线性回归推荐算法对用户评分进行预测。为了验证该算法的准确性,在Hadoop集群和亚马逊商品评分数据集上与传统的线性回归推荐算法进行了对比,实验结果表明,该算法在处理效率、推荐效果和拟合程度上有明显提高。

Big data linear regression recommendation algorithm based on scoring credibility

Aiming at the fact that traditional linear regression recommendation algorithms don’t take into account influencing factors such as user interest drift,activity,and credibility,this paper proposed a linear regression recommendation algorithm incorporated credibility ratings to further improve the accuracy of the algorithm and the fit to user preferences.Firstly,this paper comprehensively considered the user’s interest drift,activity and user evaluation information in the calculation method of user scoring credibility.Then it integrated this algorithm into the coefficient matrix solution process of traditional linear regression recommendation algorithm.Finally,it used the optimized linear regression recommendation algorithm to predict user scoring.In order to verify the accuracy of the algorithm,this paper compared the proposed algorithm with the traditional linear regression recommendation algorithms on Hadoop cluster and Amazon product scoring dataset.The experimental results show that the algorithm has significantly improved the processing efficiency,recommendation effect and fitting degree.