activity
20172023
most citedA Review-aware Graph Contrastive Learning Framework for Recommendation

169 citations · 383 across the 17 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.CL2020

R-Net: Relation of Relation Learning Network for Sentence Semantic Matching

Kun Zhang, Le Wu, Guangyi Lv +3

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recentl…

cs.IR202015 cited

Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach

Le Wu, Yonghui Yang, Kun Zhang +3

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics…

cs.IR20202 cited

Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation

Le Wu, Yonghui Yang, Lei Chen +3

With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad appli…

cs.SI2020

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Le Wu, Junwei Li, Peijie Sun +3

Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborati…

cs.IR202013 cited

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Lei Chen, Le Wu, Richang Hong +2

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations an…