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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書目名稱Database Systems for Advanced Applications影響因子(影響力)




書目名稱Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名




書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度




書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Database Systems for Advanced Applications被引頻次




書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名




書目名稱Database Systems for Advanced Applications年度引用




書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名




書目名稱Database Systems for Advanced Applications讀者反饋




書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名

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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

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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

轉(zhuǎn)折點

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

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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?..