基于深度距离分解的推荐算法研究

针对推荐系统中的矩阵分解算法只根据用户和物品的特征向量进行点积运算,无法准确衡量不同用户对物品偏好的弊端,该文提出了一种基于深度距离分解模型的推荐算法。首先,改变传统矩阵分解直接对评分值进行分解的模式,将用户与物品的评分矩阵转化为距离矩阵;然后,将距离矩阵分别按行和按列输入两个深度神经网络进行训练,得到用户和物品的距离特征向量;接下来,用距离特征向量计算用户和物品之间的距离值,通过设计的损失函数使预测距离值与真实距离值的误差达到最小;最后,将用户与物品的预测距离值转化为预测评分。实验结果表明,在不同数据集中,该文提出的基于深度距离分解模型的推荐算法在RMSE和MAE指标上均优于基线推荐算法。

A Deep Distance Factorization Based Recommendation Algorithm

To deal with the issue that the dot product adopted in matrix factorization can’t accurately measure users’ preference for items, a deep distance factorization model for recommender system is proposed. Firstly, the user-item rating matrix is converted into a distance matrix instead of being directly decomposed. Next, the distance matrix is input into two deep neural networks by row and column, and the distance feature vectors of users and items are obtained. Then, the distance between the user and the item is calculated with the distance feature vectors of users and items, and the error between predicted distance value and real distance value is minimized through the designed loss function. Finally, the ratings are converted from the predicted distance values. Experiments on different datasets show that the proposed algorithm outperforms other algorithms on rating prediction task.