2 citations · 4 across the 10 of their papers we have counts for
14 papers
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…
Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper
Gehui Chen, Guan'an Wang, Xiaowen Huang +1
Recent Video-to-Audio (V2A) generation relies on extracting semantic and temporal features from video to condition generative models. Training these models from scratch is resource…
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 Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers
Shangxi Wu, Jitao Sang
In recent years, large language models (LLMs) have made significant progress in the field of code generation. However, as more and more users rely on these models for software deve…
DenoiseRep: Denoising Model for Representation Learning
Zhengrui Xu, Guan'an Wang, Xiaowen Huang +1
The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, wh…
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…
Semantically consistent Video-to-Audio Generation using Multimodal Language Large Model
Gehui Chen, Guan'an Wang, Xiaowen Huang +1
Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduc…
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…
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang +4
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual…