Hao Ding

According to our database, Hao Ding authored at least 18 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs.

Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav Kveton

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Pre-trained Recommender Systems: A Causal Debiasing Perspective.

Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

2023

Pre-trained Recommender Systems: A Causal Debiasing Perspective.

Ziqian Lin, Hao Ding, Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang

CoRR, 2023

Personalized federated domain adaptation for item-to-item recommendation.

Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Trending Now: Modeling Trend Recommendations.

Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

2022

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems.

Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Zero-Shot Recommender Systems.

Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, Hao Wang

CoRR, 2021

2020

Temporal-Contextual Recommendation in Real-Time.

Yifei Ma, Balakrishnan (Murali) Narayanaswamy, Haibin Lin, Hao Ding

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching.

Yunsheng Bai, Hao Ding, Ken Gu, Yizhou Sun, Wei Wang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity.

Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang

CoRR, 2019

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation.

Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity.

Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Convolutional Set Matching for Graph Similarity.

Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

CoRR, 2018

Convolutional Neural Networks for Fast Approximation of Graph Edit Distance.

Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

CoRR, 2018

Graph Edit Distance Computation via Graph Neural Networks.

Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang

CoRR, 2018

Learning from Multi-View Multi-Way Data via Structural Factorization Machines.

Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

2017

Learning from Multi-View Structural Data via Structural Factorization Machines.

Chun-Ta Lu, Lifang He, Hao Ding, Philip S. Yu

CoRR, 2017

Multi-way Multi-level Kernel Modeling for Neuroimaging Classification.

Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017