123 citations · 284 across the 9 of their papers we have counts for
9 papers
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network
Chenglin Li, Yuanzhen Xie, Chenyun Yu +4
Inductive representation learning on temporal heterogeneous graphs is crucial for scalable deep learning on heterogeneous information networks (HINs) which are time-varying, such a…
AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness
Liyao Jiang, Chenglin Li, Haolan Chen +5
Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are…
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…
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Chenglin Li, Mingjun Zhao, Huanming Zhang +5
Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to g…
Similarity Embedding Networks for Robust Human Activity Recognition
Chenglin Li, Carrie Lu Tong, Di Niu +5
Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex rea…
Meta-HAR: Federated Representation Learning for Human Activity Recognition
Chenglin Li, Di Niu, Bei Jiang +2
Human activity recognition (HAR) based on mobile sensors plays an important role in ubiquitous computing. However, the rise of data regulatory constraints precludes collecting priv…
Android Malware Detection using Large-scale Network Representation Learning
Rui Zhu, Chenglin Li, Di Niu +2
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced…
Android Malware Detection based on Factorization Machine
Chenglin Li, Keith Mills, Rui Zhu +3
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users fac…