Neural collaborative filtering (NCF) and recurrent recommender systems (RRN)
have been successful in modeling user-item relational data. However, they are
also limited in their assumption of static or sequential modeling of relational
data as they do not account for evolving users' preference over time as well as
changes in the underlying factors that drive the change in user-item
relationship over time. We address these limitations by proposing a Neural
Tensor Factorization (NTF) model for predictive tasks on dynamic relational
data. The NTF model generalizes conventional tensor factorization from two
perspectives: First, it leverages the long short-term memory architecture to
characterize the multi-dimensional temporal interactions on relational data.
Second, it incorporates the multi-layer perceptron structure for learning the
non-linearities between different latent factors. Our extensive experiments
demonstrate the significant improvement in rating prediction and link
prediction on dynamic relational data by our NTF model over both neural network
based factorization models and other traditional methods.

本文提出了基于神经张量分解的动态关系数据预测模型，该模型利用长短时记忆网络来刻画关系数据中的多维时间交互作用，并结合多层感知器结构学习不同潜在因素之间的非线性关系，实验证明该模型在评级预测和链接预测方面性能显著优于神经网络因子分解模型和其他传统方法。