65 citations · 177 across the 30 of their papers we have counts for
6 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-…
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…
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…
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…