relational data.
Neural collaborative filtering and recurrent recommender systems have been successful in modeling user-item relational data.
However, they are limited as they do not account for evolving users‘ preference over time as well as changes.
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.
1. how to characterize the multi-dimensional temporal interactions.
2.
Supplementary knowledge:
1. dynamic relational data.
predictive tasks on dynamic relational data.
PP: Neural tensor factorization
原文：https://www.cnblogs.com/dulun/p/12271047.html