most citedDisentangled Graph Contrastive Learning for Review-based Recommendation

4 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

Yi Xu, Shuqian Sheng, Bo Xue +3

Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the n…

physics.soc-ph2023

Revisiting Network Value: Sublinear Knowledge Law

Xinbing Wang, Luoyi Fu, Huquan Kang +3

Three influential laws, namely Sarnoff's Law, Metcalfe's Law, and Reed's Law, have been established to describe network value in terms of the number of neighbors, edges, and subgra…

cs.IR20233 cited

Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph

Cheng Deng, Jiaxin Ding, Luoyi Fu +3

The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on b…

cs.LG20232 cited

FMGNN: Fused Manifold Graph Neural Network

Cheng Deng, Fan Xu, Jiaxing Ding +3

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while rece…

cs.CL2023

PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

Cheng Deng, Bo Tong, Luoyi Fu +4

In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial. However, domain-s…

cs.IR20224 cited

Disentangled Graph Contrastive Learning for Review-based Recommendation

Yuyang Ren, Haonan Zhang, Qi Li +5

User review data is helpful in alleviating the data sparsity problem in many recommender systems. In review-based recommendation methods, review data is considered as auxiliary inf…