融合注意力LSTM的神经张量分解推荐模型

针对结合深度学习模型的协同过滤算法未考虑关联数据的多维交互随时间动态变化的问题,该文提出一种融合时间交互学习和注意力长短期记忆网络的张量分解推荐模型(LA-NTF)。通过采用基于注意力机制的长短期记忆网络从项目文本信息中提取项目的潜在向量,然后使用融合注意力机制的长短期记忆网络来表征用户—项目关系数据在时间上的多维交互,最后将用户—项目—时间三维张量嵌入多层感知器中,学习不同潜在因子之间的非线性结构特征,从而预测用户对项目的评分。在两个真实数据集上的大量实验表明,与其他传统方法和基于神经网络的矩阵分解模型相比,方根误差(RMSE)和平均绝对误差(MAE)指标均有明显提升,说明LA-NTF模型可显著改善各种动态关系数据的评级预测任务。

Neural Tensor Factorization Recommendation Model Based on Attention LSTM

Current collaborative filtering algorithms combined with deep learning models fail to consider the problem of dynamical change over time of multi-dimension interaction of linked data. This paper proposes a tensor factorization recommendation model that combines time interaction learning and long short-term memory networks with attention (LA-NTF). Firstly, the long short-term memory network with attention mechanism is applied to extract the latent vector of the item from the item text information. Secondly, the multi-dimension interaction of user-item relational data in time is characterized by the long short-term memory networks with attention mechanism. Finally, the user-item-time 3D tensor is embedded in the multi-layer perceptron to learn the non-linear structural features between different latent factors, to predict the user's rating of the item. Experiments on two real-world datasets show that RMSE and MAE indicators significantly outperform neural network based factorization models and other traditional methods, indicating that the significant improvement in rating prediction task on various dynamic relational data by our LA-NTF model.