基于深度神经网络和加权隐反馈的个性化推荐

改进的矩阵分解(SVD++)将用户和物品特征向量的内积作为用户对物品的评分,而内积无法捕捉用户与物品之间复杂的高阶非线性关系.此外,SVD++在融入用户隐式反馈时,未区分不同交互物品对于用户特征表达的贡献.针对上述问题,文中提出基于深度神经网络和加权隐反馈的推荐算法(DeepNASVD++),采用深度神经网络建模用户与物品之间的关系,使用注意力机制计算历史交互物品在建模用户隐式反馈时的权重.在公开数据集上的实验验证文中算法的有效性.

Personalized Recommendation Algorithm Based on Deep Neural Network and Weighted Implicit Feedback

In singular value decomposition++(SVD++), inner product of user and item feature vector is regarded as user's rating of items. However, inner product cannot capture the high-order nonlinear relationship between the user and the item. In addition, the contribution of different interactive items cannot be distinguished when user's implicit feedback is incorporated in SVD++. A recommendation algorithm based on deep neural network and weighted implicit feedback is proposed to solve the two problems. Deep neural network is adopted to model the relationship between the user and the object and attention mechanism is utilized to calculate the weight of historical interactive items in modeling user's implicit feedback. Experiments on public datasets verify the effectiveness of the proposed algorithm.