17 citations · 48 across the 9 of their papers we have counts for
10 papers
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Jiazheng Jing, Yinan Zhang, Xin Zhou +1
Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through…
Unsupervised Representation Learning for Time Series: A Review
Qianwen Meng, Hangwei Qian, Yong Liu +3
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enablin…
Unifying gradient regularization for Heterogeneous Graph Neural Networks
Xiao Yang, Xuejiao Zhao, Zhiqi Shen
Heterogeneous Graph Neural Networks (HGNNs) are a class of powerful deep learning methods widely used to learn representations of heterogeneous graphs. Despite the fast development…
Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
Lei Zhang, Mingliang Wang, Xin Zhou +5
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address,…
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
Hongyu Zhou, Xin Zhou, Zhiwei Zeng +2
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit in…
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
Hongyu Zhou, Xin Zhou, Lingzi Zhang +1
User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets…
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Qianwen Meng, Hangwei Qian, Yong Liu +3
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recentl…
Inductive Graph Transformer for Delivery Time Estimation
Xin Zhou, Jinglong Wang, Yong Liu +3
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase exp…
Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
Yongwei Wang, Yong Liu, Zhiqi Shen
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to ev…
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Yongwei Wang, Yuan Li, Zhiqi Shen +1
Reliable skin cancer diagnosis models play an essential role in early screening and medical intervention. Prevailing computer-aided skin cancer classification systems employ deep l…