no code implementations • 30 Apr 2022 • Cheng-Te Li, Cheng Hsu, Yang Zhang
We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups.
1 code implementation • 16 Nov 2021 • Cheng Hsu, Cheng-Te Li, Diego Saez-Trumper, Yi-Zhan Hsu
Self-contradiction is one of the low-quality article types in Wikipedia.
1 code implementation • 22 Jun 2021 • I-Chung Hsieh, Cheng-Te Li
In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal.
1 code implementation • 18 Jun 2021 • Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks.
no code implementations • 17 Jun 2021 • I-Chung Hsieh, Cheng-Te Li
The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel.
1 code implementation • NAACL 2021 • Chih-Yao Chen, Cheng-Te Li
While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.
Ranked #2 on
Zero-shot Relation Classification
on Wiki-ZSL
1 code implementation • 29 Jan 2021 • Cheng Hsu, Cheng-Te Li
In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings.
no code implementations • 29 Jan 2021 • Yi-Ju Lu, Cheng-Te Li
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems.
1 code implementation • 26 Oct 2020 • Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Specifically, RL-HGNN models the meta-path design process as a Markov Decision Process and uses a policy network to adaptively design a meta-path for each node to learn its effective representations.
1 code implementation • EMNLP 2020 • Hsin-Yu Chen, Cheng-Te Li
In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions.
1 code implementation • 1 Oct 2020 • Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso- and macro-level semantic information in the graph.
1 code implementation • 8 Sep 2020 • Zhiqiang Zhong, Cheng-Te Li, Jun Pang
To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework.
1 code implementation • KDD 2020 • Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
1 code implementation • ACL 2020 • Yi-Ju Lu, Cheng-Te Li
This paper solves the fake news detection problem under a more realistic scenario on social media.
1 code implementation • 26 Feb 2020 • Sungwon Han, Yizhan Xu, Sungwon Park, Meeyoung Cha, Cheng-Te Li
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.
no code implementations • ICLR 2018 • Yiping Song, Rui Yan, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao
Human-computer conversation systems have attracted much attention in Natural Language Processing.
no code implementations • 21 Nov 2017 • Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether.