Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

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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

信徒

0302-9743 D consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network..978-3-031-30671-6978-3-031-30672-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

载货清单

压迫

Who Is That Man? Lad Trouble in ,, and d information aggregation module to accurately learn item-level relation features from a knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our KRec-C2 model over existing state-of-the-art methods.

极端的正确性

KRec-C2: A Knowledge Graph Enhanced Recommendation with Context Awareness and Contrastive Learningd information aggregation module to accurately learn item-level relation features from a knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our KRec-C2 model over existing state-of-the-art methods.

都相信我的话

都相信我的话

墙壁

Culmination

Thompson Sampling with Time-Varying Reward for Contextual Banditsalgorithm. Extensive empirical experiments on two real-world datasets show that our proposed algorithm outperforms state-of-the-art time-varying bandit algorithms. Furthermore, the designed reward mechanism can be flexibly configured to other bandit algorithms to improve them.

善于

Cold-Start Based Multi-scenario Ranking Model for Click-Through Rate Predictionn attention mechanism; and 2) User Representation Memory Network (URMN), which benefits cold-start users from users with rich behaviors through a memory read and write mechanism. CSMN seamlessly integrates both components in an end-to-end learning framework. Extensive experiments on real-world offli