引入时间效应的SVD++线性回归推荐算法

SVD++ Linear Regression Recommendation Algorithm with Time Effect

针对传统协同过滤算法中的数据稀疏问题,在SVD++算法和线性回归模型的基础上引入时间效应属性,提出一种推荐算法timeSVD++LR。采用SVD++算法将用户和项目信息与隐式反馈信息相融合映射到隐语义空间,将用户和项目之间的交互作用建模为该空间中的内积。通过描述用户和物品在各因子上的特征来解释评分值,在此基础上对时间效应建模,进一步提高预测结果的准确度。根据预测评分矩阵构造特征向量,将原始训练数据作为线性回归模型的输入,采用梯度下降算法优化最终代价函数,生成使得代价函数值最小的参数向量,同时将特征向量和参数向量代入预测模型求解预测评分。在MovieLens数据集上的实验结果表明,与RSVD、SVD++和timeSVD++算法相比,该算法的平均绝对误差和均方根误差均较低,其推荐准确性较高。

Aiming at the problem of data sparsity in traditional collaborative filtering algorithm,this paper proposes a recommended algorithm named timeSVD++LR,which introduces the time effect attribute on the basis of the SVD++algorithm and linear regression model.The SVD++algorithm is used to map the user and item information fusion implicit feedback information to the implicit semantic space,and the user-item interaction is modeled as the inner product of the space.The scoring value is explained by describing the characteristics of users and items on various factors,and then the time effect is modeled to further improve the accuracy of the prediction results.At the same time,the eigenvector is constructed according to the prediction scoring matrix.The original training data is used as the input of the linear regression model,and the final cost function is optimized by gradient descent algorithm to generate the parameter vector that minimizes the value of the cost function.The eigenvector and parameter vector are brought into the prediction model to solve the prediction score.Experimental results on the MovieLens dataset show that,compared with the RSVD,SVD++and timeSVD++algorithms,the average absolute error and root mean square error of the proposed algorithm are lower,and its recommendation accuracy is higher.