56 citations · 194 across the 14 of their papers we have counts for
14 papers
A Versatile Graph Learning Approach through LLM-based Agent
Lanning Wei, Huan Zhao, Xiaohan Zheng +2
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve…
Exploring the Feasibility of ChatGPT for Event Extraction
Jun Gao, Huan Zhao, Changlong Yu +1
Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challeng…
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
Jun Gao, Changlong Yu, Wei Wang +2
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can genera…
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Jun Gao, Wei Wang, Changlong Yu +3
Representations of events described in text are important for various tasks. In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering fra…
Simplifying Architecture Search for Graph Neural Network
Huan Zhao, Lanning Wei, Quanming Yao
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural archit…
Vertex-reinforced Random Walk for Network Embedding
Wenyi Xiao, Huan Zhao, Vincent W. Zheng +1
In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW),…
Ranking Users in Social Networks with Motif-based PageRank
Huan Zhao, Xiaogang Xu, Yangqiu Song +3
PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which r…
Motif Enhanced Recommendation over Heterogeneous Information Network
Huan Zhao, Yingqi Zhou, Yangqiu Song +1
Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and…
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
Wenyi Xiao, Huan Zhao, Haojie Pan +3
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interestin…
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Qiwei Chen, Huan Zhao, Wei Li +2
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dim…
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Chao Li, Zhiyuan Liu, Mengmeng Wu +7
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves ca…
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
Jizhe Wang, Pipei Huang, Huan Zhao +3
Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. The billio…
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
Huan Zhao, Quanming Yao, Yangqiu Song +2
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behavi…
Collaborative Filtering with Social Local Models
Huan Zhao, Quanming Yao, James T. Kwok +1
Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restric…