39 citations · 58 across the 20 of their papers we have counts for
20 papers
StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance
Guanming Liu, Yuqi Ren, Hansu Gu +5
A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent obs…
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…
Hyena Operator for Fast Sequential Recommendation
Jiahao Liu, Lin Li, Zhiyuan Li +3
Sequential recommendation models, particularly those based on attention, achieve strong accuracy but incur quadratic complexity, making long user histories prohibitively expensive.…
RQ-GMM: Residual Quantized Gaussian Mixture Model for Multimodal Semantic Discretization in CTR Prediction
Ziye Tong, Jiahao Liu, Weimin Zhang +7
Multimodal content is crucial for click-through rate (CTR) prediction. However, directly incorporating continuous embeddings from pre-trained models into CTR models yields suboptim…
Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction
Jiahao Liu, Hongji Ruan, Weimin Zhang +7
This paper explores effective numerical feature embedding for Click-Through Rate prediction in streaming environments. Conventional static binning methods rely on offline statistic…
Dynamic Feature-Embedding Communication via Codebook Distillation for Federated Recommendation
Mingzhe Han, Jiahao Liu, Dongsheng Li +6
Federated recommendation systems commonly protect user privacy by keeping user parameters on local devices, while exchanging item parameters for collaborative model training. Howev…
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…
Improving LLM-powered Recommendations with Personalized Information
Jiahao Liu, Xueshuo Yan, Dongsheng Li +6
Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline…
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations
Jiahao Liu, Shengkang Gu, Dongsheng Li +7
LLM-based user agents, which simulate user interaction behavior, are emerging as a promising approach to enhancing recommender systems. In real-world scenarios, users' interactions…
Unbiased Collaborative Filtering with Fair Sampling
Jiahao Liu, Dongsheng Li, Hansu Gu +4
Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular…
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-…
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu, Jiahao Liu, Hansu Gu +4
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of ite…
Recommendation Unlearning via Matrix Correction
Jiahao Liu, Dongsheng Li, Hansu Gu +5
Recommender systems are important for providing personalized services to users, but the vast amount of collected user data has raised concerns about privacy (e.g., sensitive data),…
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Jiahao Liu, Dongsheng Li, Hansu Gu +4
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, seque…
Personalized Graph Signal Processing for Collaborative Filtering
Jiahao Liu, Dongsheng Li, Hansu Gu +4
The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the obser…
Parameter-free Dynamic Graph Embedding for Link Prediction
Jiahao Liu, Dongsheng Li, Hansu Gu +3
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for li…