activity
20152022
most citedKnowledge Graph Convolutional Networks for Recommender Systems

1k citations · 1.9k across the 11 of their papers we have counts for

collaborators

16 papers

cs.CL2022

Few-shot Query-Focused Summarization with Prefix-Merging

Ruifeng Yuan, Zili Wang, Ziqiang Cao +1

Query-focused summarization has been considered as an important extension for text summarization. It aims to generate a concise highlight for a given query. Different from text sum…

cs.CL20201 cited

Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT

Ruifeng Yuan, Zili Wang, Wenjie Li

Most current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that th…

cs.LG201925 cited

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

Hongwei Wang, Fuzheng Zhang, Mengdi Zhang +4

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could…

cs.LG20193 cited

When Collaborative Filtering Meets Reinforcement Learning

Yu Lei, Wenjie Li

In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the…

cs.IR20191k 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.IR20192 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…