34 citations · 40 across the 10 of their papers we have counts for
14 papers
Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks
Chuyifei Zhang, Hongyu Cui, Xiaowen Huang +1
Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not control positional placement of ta…
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…
When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Ming Tang, Xiaowen Huang, Jitao Sang
Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and…
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…
A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems
Lixi Zhu, Xiaowen Huang, Jitao Sang
Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personaliz…
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…
Exploring the Privacy Protection Capabilities of Chinese Large Language Models
Yuqi Yang, Xiaowen Huang, Jitao Sang
Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence. Yet, these advancements have raised…
How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation
Lixi Zhu, Xiaowen Huang, Jitao Sang
Conversational Recommender System (CRS) interacts with users through natural language to understand their preferences and provide personalized recommendations in real-time. CRS has…
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis
Yaogong Feng, Xiaowen Huang, Pengbo Yang +2
Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem. Most models achieve competitive performance but still su…
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…
Adversarial Privacy-preserving Filter
Jiaming Zhang, Jitao Sang, Xian Zhao +3
While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e.g.,…