32 citations · 137 across the 30 of their papers we have counts for
30 papers
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Shaowei Wei, Zhengwei Wu, Xin Li +5
Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. T…
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang +4
Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing…
Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
Daixin Wang, Zhiqiang Zhang, Yeyu Zhao +3
User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayme…
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Junjie Wang, Dan Yang, Binbin Hu +3
In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this is…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
Chunjing Gan, Binbin Hu, Bo Huang +6
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspe…
Long-tail Augmented Graph Contrastive Learning for Recommendation
Qian Zhao, Zhengwei Wu, Zhiqiang Zhang +1
Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usu…
Generative Contrastive Graph Learning for Recommendation
Yonghui Yang, Zhengwei Wu, Le Wu +5
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers hav…
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs
Dalong Zhang, Xianzheng Song, Zhiyang Hu +6
GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge…
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Kun Zhang, Le Wu, Guangyi Lv +6
Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently…
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
Dan Yang, Binbin Hu, Xiaoyan Yang +4
With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users th…
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Sicong Xie, Binbin Hu, Fengze Li +4
Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which gr…
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
Weifan Wang, Binbin Hu, Zhicheng Peng +5
Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience…
Improving Recommendation Fairness via Data Augmentation
Lei Chen, Le Wu, Kun Zhang +5
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the…
Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce
Borui Ye, Shuo Yang, Binbin Hu +5
E-commerce has gone a long way in empowering merchants through the internet. In order to store the goods efficiently and arrange the marketing resource properly, it is important fo…
Neural Graph Matching for Pre-training Graph Neural Networks
Yupeng Hou, Binbin Hu, Wayne Xin Zhao +3
Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-s…
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Qian Zhao, Shuo Yang, Binbin Hu +5
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area. The WSDM Cup 2022 seeks for solutions that pre…
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Hongrui Liu, Binbin Hu, Xiao Wang +3
Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful trai…
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Lingxiao Wang, Ziqi Liu +4
Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling…
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu, Ziqi Liu, Ge Jin +4
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are mac…
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Kai Zhang, Hao Qian, Qi Liu +4
Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively stu…
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
Tianchi Cai, Daxi Cheng, Chen Liang +6
A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B tes…
Heterogeneous Graph Neural Network for Recommendation
Jinghan Shi, Houye Ji, Chuan Shi +3
The prosperous development of e-commerce has spawned diverse recommendation systems. As a matter of fact, there exist rich and complex interactions among various types of nodes in…