activity
20142020
most citedConditional Restricted Boltzmann Machines for Cold Start Recommendations

6 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20204 cited

Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network

Yuanyuan Jin, Wei Zhang, Xiangnan He +2

Herb recommendation plays a crucial role in the therapeutic process of Traditional Chinese Medicine(TCM), which aims to recommend a set of herbs to treat the symptoms of a patient.…

cs.CL20203 cited

Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Qianming Xue, Wei Zhang, Hongyuan Zha

Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. Most existing relevant m…

cs.LG20192 cited

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

Zhuo Wang, Wei Zhang, Ning Liu +1

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance,…

cs.LG20191 cited

Learning Robust Representations with Graph Denoising Policy Network

Lu Wang, Wenchao Yu, Wei Wang +5

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing p…

cs.IR20146 cited

Conditional Restricted Boltzmann Machines for Cold Start Recommendations

Jiankou Li, Wei Zhang

Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold star…