Titlebook: Web Information Systems and Applications; 20th International C Long Yuan,Shiyu Yang,Xiang Zhao Conference proceedings 2023 The Editor(s) (i

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GENE: Global Enhanced Graph Neural Network Embedding for?Session-Based Recommendationsed on the order in which the items interact in the session with normalization. Second, we employ a graph neural network to obtain the latent vectors of items, then we represent the session graph by attention mechanisms. Third, we explore the session representation fusion for prediction incorporatin

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恃強凌弱

Research on Predicting the Impact of Venue Based on Academic Heterogeneous Networkattribute features effectively. To solve the above problems, we propose a hybrid model of academic heterogeneous network representation learning combined with multivariate random walk, termed as AHRV. The specific content is to mine the heterogeneous local network information of nodes in the academi

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Knowledge Graph Completion with?Fused Factual and?Commonsense Information based on the analytic hierarchy process. The obtained commonsense can further improve the quality of negative samples and the effectiveness of link prediction. Experimental results on four datasets of the knowledge graph completion (KGC) task show that our method can improve the performance of the original knowledge graph embedding (KGE) model.

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Finding Introverted Cores in?Bipartite Graphss inside focus on the products in the subgraph. We propose an .(.) algorithm to compute the .-core with given . and .. Besides, we introduce an efficient algorithm to decompose a graph by the .-core. The experiments on real-world data demonstrate that our model is effective and our proposed algorithms are efficient.

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