Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems-猫眼课题宝

DOI：

10.24963/ijcai.2020/339

发表时间：

2020-07

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Huiyuan Chen;Jing Li

推荐系统往往涉及多方面的因素。例如，在网上购买鞋子时，消费者通常会在做出决定之前查看他们的图像，评级和产品评论。为了学习多方面的因素，已经开发了许多基于张量因子分解的上下文感知模型。然而，现有的模型假设张量数据中的多线性结构，从而无法捕获非线性特征相互作用。为了填补这一空白，我们提出了一种新的非线性张量机，它结合了深度神经网络和张量代数来捕捉多方面因素之间的非线性相互作用。我们进一步考虑对抗学习来帮助我们模型的训练。大量的实验证明了该模型的有效性。

Recommender systems often involve multi-aspect factors. For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. However, existing models assume multilinear structures in the tensor data, thus failing to capture nonlinear feature interactions. To fill this gap, we propose a novel nonlinear tensor machine, which combines deep neural networks and tensor algebra to capture nonlinear interactions among multi-aspect factors. We further consider adversarial learning to assist the training of our model. Extensive experiments demonstrate the effectiveness of the proposed model.

Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems

文献摘要

相似文献