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

Lipoprotein

DAFT

Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Rrogeneous 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.

BACLE

Enhancing Graph Convolution Network for Novel Recommendationtrue negative popular samples. Extensive experimental results on three datasets demonstrate that our method outperforms both graph based and novelty oriented baselines by a large margin in terms of the overall F-measure.

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gratify

Exterior

PMAR: Multi-aspect Recommendation Based on Psychological Gaps overall and personalized psychological gaps. Specifically, we first design a gap logit unit for learning the user’s overall psychological gap towards items derived from textual review and merchant’s description. We then integrate a user-item co-attention mechanism to calculate the user’s personali

anus928

COW

蕨類

Multi-view Multi-behavior Contrastive Learning in?Recommendation differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on ..

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