32 citations · 35 across the 7 of their papers we have counts for
7 papers
Selection and Exploitation of High-Quality Knowledge from Large Language Models for Recommendation
Guanchen Wang, Mingming Ha, Tianbao Ma +4
In recent years, there has been growing interest in leveraging the impressive generalization capabilities and reasoning ability of large language models (LLMs) to improve the perfo…
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
Mingming Ha, Xuewen Tao, Wenfang Lin +3
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are gene…
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Wujiang Xu, Xuying Ning, Wenfang Lin +7
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches a…
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang +5
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CD…
Neural Node Matching for Multi-Target Cross Domain Recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha +5
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) s…
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
Xuewen Tao, Mingming Ha, Xiaobo Guo +3
Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of mul…
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Ying Chen, Siwei Qiang, Mingming Ha +5
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse sce…