34 citations · 40 across the 10 of their papers we have counts for
7 papers · 1 filter
Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm
Li Cuihong, Huang Xiaowen, Yin Chuanhuan +1
Membership Inference Attack (MIA) aims to determine whether a specific data sample was included in the training dataset of a target model. Traditional MIA approaches rely on shadow…
Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems
Xin Wang, Xiaowen Huang, Jitao Sang
Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To…
Mitigating Filter Bubble from the Perspective of Community Detection: A Universal Framework
Ming Tang, Xiaowen Huang, Jitao Sang
In recent years, recommender systems have primarily focused on improving accuracy at the expense of diversity, which exacerbates the well-known filter bubble effect. This paper pro…
ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations
Zekun Liu, Xiaowen Huang, Jitao Sang
Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommender systems, due to the inherent struc…
Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library
Lei Cheng, Xiaowen Huang, Jitao Sang +1
Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natur…
Learning to Learn a Cold-start Sequential Recommender
Xiaowen Huang, Jitao Sang, Jian Yu +1
The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendatio…
Knowledge Graph-enhanced Sampling for Conversational Recommender System
Mengyuan Zhao, Xiaowen Huang, Lixi Zhu +2
The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimatio…