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Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-11-30 , DOI: 10.1155/2020/8812370
Qian Gao 1 , Pengcheng Ma 1
Affiliation  

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Therefore, it is hard to get the accurate results of the user behavior prediction. In this paper, a graph structure is used to establish the interaction between context and users/items. Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users. In particular, we construct a context-user and context-item interactions graph separately. In the interactions graph, each node is composed of a user feature field, an item feature field, and a feature field of different contexts. Different feature fields can interact through edges. Therefore, the task of modeling feature interaction can be transformed into modeling the node interaction on the corresponding graph. To this end, an innovative model called context-aware graph neural network (CA-GNN) model is designed. Furthermore, in order to obtain more accurate and efficient recommendation results, first, we innovatively use the attention mechanism to improve the interpretability of CA-GNN; second, we innovatively use the degree of physical fatigue features which has never been used in traditional CARS as critical contextual feature information into our CA-GNN. We simulated the Food and Yelp datasets. The experimental results show that CA-GNN is better than other methods in terms of root mean square error (RMSE) and mean absolute error (MAE).

中文翻译:

基于图神经网络和上下文感知的用户行为预测与推荐系统研究

由于上下文信息对用户行为的影响,上下文感知推荐系统(CARS)近年来引起了广泛关注。最先进的上下文感知推荐系统将原始的多字段特征映射到共享的隐藏空间中,然后将其简单地连接到深度神经网络(DNN)或其他专门设计的网络。但是,对于不同的区域,以足够灵活和明确的方式对复杂交互进行建模的能力受到要素字段简单非结构化组合的限制。因此,难以获得用户行为预测的准确结果。在本文中,使用图结构来建立上下文与用户/项目之间的交互。通过对用户行为进行建模,我们可以探索不同上下文环境中的用户偏好,以便为用户提供个性化推荐。特别是,我们分别构造了一个上下文用户和上下文项交互图。在交互图中,每个节点由用户特征字段,项目特征字段和不同上下文的特征字段组成。不同的要素字段可以通过边进行交互。因此,建模特征交互的任务可以转换为在相应图上建模节点交互的任务。为此,设计了一种称为上下文感知图神经网络(CA-GNN)模型的创新模型。此外,为了获得更准确和有效的推荐结果,首先,我们创新地使用注意力机制来提高CA-GNN的可解释性。第二,我们创新地使用了在传统CARS中从未使用过的身体疲劳特征的程度作为CA-GNN中的关键上下文特征信息。我们模拟了Food和Yelp数据集。实验结果表明,CA-GNN在均方根误差(RMSE)和平均绝对误差(MAE)方面优于其他方法。
更新日期:2020-12-01
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