85 citations · 419 across the 56 of their papers we have counts for
7 papers · 1 filter
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Weijieying Ren, Lei Wang, Kunpeng Liu +3
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-t…
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Meng Xiao, Ziyue Qiao, Yanjie Fu +5
The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has b…
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
Dongjie Wang, Kunpeng Liu, Hui Xiong +1
In this paper, we focus on the problem of modeling dynamic geo-human interactions in streams for online POI recommendations. Specifically, we formulate the in-stream geo-human inte…
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
Ziyue Qiao, Yanjie Fu, Pengyang Wang +5
With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become in…
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…
A Neural Influence Diffusion Model for Social Recommendation
Le Wu, Peijie Sun, Yanjie Fu +3
Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item em…
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Le Wu, Peijie Sun, Richang Hong +3
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popu…