Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

Notorious

MANE: A Multi-cascade Adversarial Network Embedding Model for?Anchor Link Predictions for correspondence matching. Extensive experiments on real-world social network datasets demonstrate that our method can achieve the expected performance, especially in improving the top-1 precision and recall.

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Bucket

GPSR: Graph Prompt for?Session-Based Recommendation. Specifically, we first study the item transition pattern by constructing session graphs, based on which the GNN?model is pretrained. Then, we introduce a universal prompt-based tuning method called Graph Prompt Feature (GPF), for adapting?the pretrained GNN model to the downstream session-based re

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INERT

Global Route Planning for?Large-Scale Requests on Traffic-Aware Road Networkgroup them together. In this way, only the conflicts within each group need to be resolved in a local area, so the efficiency is improved. Additionally, several alternative paths are calculated and the global optimal routes are found in finite iterations. Extensive experiments conducted on real-worl

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聯(lián)想

,Verzweigter Stromübertritt in die Erde,. Specifically, We use LightGCN to learn user and item embeddings,?and then we combine multi-task learning with contrastive learning?to explicitly exploit behavioral dependence in embeddings learning?and capture differences between embeddings. We conduct comprehensive experiments on two real-world d

Emg827

https://doi.org/10.1007/978-3-662-41795-9ified heterogeneous graph, creating the heterogeneous view. We?also construct the social relation enhanced view by resampling?the user-item interaction graph. In the learning process, we leverage meta-path based graph learning and graph diffusion with attention?to obtain multi-view embeddings for us