activity
20162024
most citedKGAT: Knowledge Graph Attention Network for Recommendation

2.2k citations · 7.3k across the 85 of their papers we have counts for

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Showing 2019Show all

13 papers · 1 filter

cs.SI2019★ 1 cited

Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

Haozhe Wu, Zhiyuan Hu, Jia Jia +3

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches…

cs.CL2019

Improving Neural Relation Extraction with Implicit Mutual Relations

Jun Kuang, Yixin Cao, Jianbing Zheng +3

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing metho…

cs.IR2019★ 5 cited

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

Xiaoyu Du, Xiangnan He, Fajie Yuan +3

As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, an…

cs.LG2019★ 2.2k cited

KGAT: Knowledge Graph Attention Network for Recommendation

Xiang Wang, Xiangnan He, Yixin Cao +2

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional…

cs.CV2019★ 198 cited

Learning to Compose and Reason with Language Tree Structures for Visual Grounding

Richang Hong, Daqing Liu, Xiaoyu Mo +2

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend…

cs.IR2019

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Fajie Yuan, Xiangnan He, Haochuan Jiang +4

Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or…