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Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

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樓主: Waterproof
31#
發(fā)表于 2025-3-26 23:47:43 | 只看該作者
32#
發(fā)表于 2025-3-27 01:41:11 | 只看該作者
Hyperbolic Personalized Tag Recommendationovel PTR model that operates on hyperbolic space, namely HPTR. HPTR learns the representations of entities by modeling their interactive relationships in hyperbolic space and utilizes hyperbolic distance to measure semantic relevance between entities. Specially, we adopt tangent space optimization t
33#
發(fā)表于 2025-3-27 05:30:11 | 只看該作者
Diffusion-Based Graph Contrastive Learning for?Recommendation with?Implicit Feedbacks. A symmetric contrastive learning objective is used to contrast local node representations of the diffusion graph with those of the user-item interaction graph for learning better user and item representations. Extensive experiments on real datasets demonstrate that GDCL consistently outperforms s
34#
發(fā)表于 2025-3-27 11:48:11 | 只看該作者
Multi-behavior Recommendation with?Two-Level Graph Attentional Networksn, we learn the dynamic feature of target users and target items by modeling the dependency relation between them. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art recommendation methods.
35#
發(fā)表于 2025-3-27 15:27:41 | 只看該作者
36#
發(fā)表于 2025-3-27 19:42:25 | 只看該作者
37#
發(fā)表于 2025-3-27 22:07:54 | 只看該作者
César Fernández-de-las-Pe?as,Kimberly Bensen and fuse users’ personalized preferences on different modalities with a multi-modal probabilistic graph. Then, to filter out irrelevant and redundant information in multi-modal data, we extend the information bottleneck theory from single-modal to multi-modal scenario and design a multi-modal infor
38#
發(fā)表于 2025-3-28 04:55:30 | 只看該作者
Occlusal Diagnosis and Treatment of TMDrogeneous graphs from ratings and reviews to preserve inter- and intra-domain relations. Then, a relation-aware graph convolutional network is designed to simultaneously distill domain-shared and domain-specific features, by exploring the multi-hop heterogeneous connections across different graphs.
39#
發(fā)表于 2025-3-28 10:13:28 | 只看該作者
40#
發(fā)表于 2025-3-28 10:59:50 | 只看該作者
https://doi.org/10.1007/978-3-319-99909-8l, we not only design a personalized controller to enhance the deep knowledge tracing model for modeling learner’s forgetting behavior, but also use personality to model the individual differences based on the theory of cognitive psychology. In CRT, we adaptively combine learner’s knowledge level ob
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