10 citations · 31 across the 38 of their papers we have counts for
10 papers · 1 filter
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
Jiafeng Xia, Dongsheng Li, Hansu Gu +4
Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past…
DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship
Yaqiong Li, Peng Zhang, Hansu Gu +5
Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process,…
Filtering Discomforting Recommendations with Large Language Models
Jiahao Liu, Yiyang Shao, Peng Zhang +6
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-…
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation
Chenyu Wang, Shuo Yan, Yixuan Chen +10
Video generation using diffusion-based models is constrained by high computational costs due to the frame-wise iterative diffusion process. This work presents a Diffusion Reuse MOt…
GraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering
Jiafeng Xia, Dongsheng Li, Hansu Gu +2
Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering tasks due to their ability to extract powerful structural features. However, combining the g…
Role Identification based Method for Cyberbullying Analysis in Social Edge Computing
Runyu Wang, Tun Lu, Peng Zhang +1
Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators,…
AOTree: Aspect Order Tree-based Model for Explainable Recommendation
Wenxin Zhao, Peng Zhang, Hansu Gu +3
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recomme…
Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
Yubin Shi, Yixuan Chen, Mingzhi Dong +10
Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained,…
Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization
Shitong Duan, Xiaoyuan Yi, Peng Zhang +5
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduce…
Frequency-aware Graph Signal Processing for Collaborative Filtering
Jiafeng Xia, Dongsheng Li, Hansu Gu +4
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the im…