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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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

載貨清單

壓迫

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.

極端的正確性

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.

都相信我的話

都相信我的話

墻壁

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