activity
20172021
most citedLearning Intents behind Interactions with Knowledge Graph for Recommendation

579 citations · 632 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SI202148 cited

Click-Through Rate Prediction with Multi-Modal Hypergraphs

Li He, Hongxu Chen, Dingxian Wang +3

Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) pr…

cs.SI20212 cited

TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories

Li He, Dingxian Wang, Hanzhang Wang +2

Hashtag, a product of user tagging behavior, which can well describe the semantics of the user-generated content personally over social network applications, e.g., the recently pop…

cs.IR2021579 cited

Learning Intents behind Interactions with Knowledge Graph for Recommendation

Xiang Wang, Tinglin Huang, Dingxian Wang +4

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…

cs.LG2018

RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

Liping Li, Wei Xu, Tianyi Chen +2

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine worker…

cs.IR20173 cited

BiRank: Towards Ranking on Bipartite Graphs

Xiangnan He, Ming Gao, Min-Yen Kan +1

The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we…