60 citations · 127 across the 8 of their papers we have counts for
8 papers
MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation
Xiangjin Xie, Yuxin Chen, Ruipeng Wang +8
Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relation…
OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
Yuanzhen Xie, Tao Xie, Mingxiong Lin +7
In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there…
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Chenglin Li, Yuanzhen Xie, Chenyun Yu +5
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing tech…
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li +9
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets of…
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Jie Wang, Fajie Yuan, Mingyue Cheng +6
Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) commu…
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
Huiting Hong, Hantao Guo, Yucheng Lin +3
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.…
Interpretable Text Classification Using CNN and Max-pooling
Hao Cheng, Xiaoqing Yang, Zang Li +2
Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the i…
AHINE: Adaptive Heterogeneous Information Network Embedding
Yucheng Lin, Xiaoqing Yang, Zang Li +1
Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensiona…