1 citations · 2 across the 8 of their papers we have counts for
12 papers
From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents
Jiahao Liu, Mingzhe Han, Guanming Liu +6
Personalization has traditionally depended on platform-specific user models that are optimized for prediction but remain largely inaccessible to the people they describe. As LLM-ba…
Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaboration
Chi Zhang, Zhipeng Xu, Jiahao Liu +5
While personalized recommender systems excel at content discovery, they frequently expose users to undesirable or discomforting information, highlighting the critical need for user…
Drift-Aware Continual Tokenization for Generative Recommendation
Yuebo Feng, Jiahao Liu, Mingzhe Han +5
Generative recommendation commonly adopts a two-stage pipeline in which a learnable tokenizer maps items to discrete token sequences (i.e. identifiers) and an autoregressive genera…
Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models
Jiongran Wu, Jiahao Liu, Dongsheng Li +7
Large language models (LLMs) have demonstrated exceptional performance in understanding and generating semantic patterns, making them promising candidates for sequential recommenda…
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems
Shengkang Gu, Jiahao Liu, Dongsheng Li +7
Recommender systems (RS) are increasingly vulnerable to shilling attacks, where adversaries inject fake user profiles to manipulate system outputs. Traditional attack strategies of…
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
Mingzhe Han, Dongsheng Li, Jiafeng Xia +5
Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms t…