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

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书目名称Database Systems for Advanced Applications影响因子(影响力)




书目名称Database Systems for Advanced Applications影响因子(影响力)学科排名




书目名称Database Systems for Advanced Applications网络公开度




书目名称Database Systems for Advanced Applications网络公开度学科排名




书目名称Database Systems for Advanced Applications被引频次




书目名称Database Systems for Advanced Applications被引频次学科排名




书目名称Database Systems for Advanced Applications年度引用




书目名称Database Systems for Advanced Applications年度引用学科排名




书目名称Database Systems for Advanced Applications读者反馈




书目名称Database Systems for Advanced Applications读者反馈学科排名

消极词汇

懦夫

Higher-Order Graph Contrastive Learning for Recommendationom the original graph to enhance supervisory signals. Specifically, we construct two contrasting views: higher-order and general views. In the higher-order view, we devise a high-order symmetric contrastive scheme to better explore higher-order dependencies. For the general view, the objective is to

植物群

Individual

转折点

Multi-level Contrastive Learning on Weak Social Networks for Information Diffusion Predictionn. To facilitate user representation learning under sparse labels and insufficient features, we further propose self-supervised training specifically tailored for social networks with weak information. In the second stage, the cascade representations are learned using the multi-head self-attention n

转折点

BiasRec: A General Bias-Aware Social Recommendation Model initially constructs a bias matrix for each user and item, calculates bias scores, and removes them from the raw rating data. Subsequently, the debiased data is fed into a GNN to learn users’ genuine preferences. Last, it reasonably combines biases and preferences to make predictions. We performed

生存环境

激怒某人

利用

Learning Social Graph for Inactive User Recommendationring model training, which improves the construction of new edges for inactive users. Extensive experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation. Our code is available at ..