activity
20172023
most citedRippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

1.2k citations · 3.1k across the 15 of their papers we have counts for

collaborators

15 papers

cs.CL2023★ 1 cited

Unsupervised Multi-document Summarization with Holistic Inference

Haopeng Zhang, Sangwoo Cho, Kaiqiang Song +4

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised…

cs.CL2023★ 1 cited

LASER: LLM Agent with State-Space Exploration for Web Navigation

Kaixin Ma, Hongming Zhang, Hongwei Wang +3

Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. While achieving decent performance, previous methods implicit…

cs.LG2020★ 100 cited

Unifying Graph Convolutional Neural Networks and Label Propagation

Hongwei Wang, Jure Leskovec

Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification but LPA propagates n…

cs.IR2019★ 1k cited

Knowledge Graph Convolutional Networks for Recommender Systems

Hongwei Wang, Miao Zhao, Xing Xie +2

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and desi…

cs.LG2019★ 39 cited

Lipschitz Generative Adversarial Nets

Zhiming Zhou, Jiadong Liang, Yuxuan Song +5

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function…

cs.IR2019★ 2 cited

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

Hongwei Wang, Fuzheng Zhang, Miao Zhao +3

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to a…