As a response, we propose a novel behavior-aware graph neural network (BGNN) for HSBR. Our BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a session. Moreover, our BGNN integrates the information of both homogeneous ...
Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research. 来自 EBSCO 喜欢 0 阅读量: 172 作者:Q Gao,P Ma 摘要: Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention ...
The interaction context is modeled as a graph structure, and series of base host features are first extracted from the graph, and then a graph neural network (GNN) is utilized to generate enhanced host features, where latent interaction patterns are mined through the high-order structural ...
New Networks Study Findings Have Been Reported by Investigators at Shenzhen University (Bgnn: Behavior-aware Graph Neural Network for Heterogeneous Session-based Recommendation)ShenzhenPeople’s Republic of ChinaAsiaNetworksNeural NetworksShenzhen University...
Graph neural networkCognitive diagnosis,which aims to diagnose students'knowledge proficiency,is crucial for numerous online education applications,such as personalized exercise recommendation.Existing methods in this area mainly exploit students'exercising records,which ignores students'full learning process in ...
Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos. In particular, Sagittarius differentiates between different user behaviors by weighting and fuses the semantics of user behaviors into the embeddings of users and videos. Moreover, Sagittarius ...
First, we build an interaction behavior graph for multi-level and\nmulti-category data. Second, we apply DeepWalk on the behavior graph to obtain\nentity semantics, then build a graph-based convolutional neural network called\nG-CNN to learn news representations, and an attention-based LSTM to...
Prior approaches primarily focus on devising sophisticated neural architectures to encode the historical interactions of nodes, enabling capturing the specific behavior patterns. However, such methods often overlook the fact that different nodes in a graph may exhibit distinct evolutionary preferences, ...
Therefore, XNBAD utilizes a graph neural network (GNN) to automatically generate high-order features from series of extracted base ones. We evaluated the detection performance of XNBAD in a publicly available benchmark dataset ISCX-2012. To report detailed and precise experimental r...
First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to ...