Abstract
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).
Highlights
It is fundamental to establish a model to capture the user’s interest and interaction with the items in the recommendation system
FiGNN uses a graph structure to intuitively represent the characteristics of multiple fields, in which each node corresponds to a feature field, and different fields can interact through edges. erefore, the task of modeling feature interaction can be transformed into modeling the interaction of nodes on the corresponding graph. e context-aware graph neural network (CA-graph neural network (GNN)) model in this paper is based on Fi-GNN model
When we only model context-item interactions to get prediction results, CA-GNN-Item represents CA-GNN that only models
Summary
It is fundamental to establish a model to capture the user’s interest and interaction with the items in the recommendation system. A user usually likes to listen to some electronic passion songs, but when he is at home with his wife at night and feels relaxed, he prefers to listen to warm love songs. The additional context information [1] such as the degree of physical fatigue and other contextual information on the interaction with the users/ items plays a critical role in the accuracy for all types of recommendation systems. The black arrows represent different contextual information that can affect the user’s interests. e green arrow indicates the user’s personal interest, and the red arrow represents the change of the user’s interest as the context changes
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