6 citations · 25 across the 33 of their papers we have counts for
35 papers
FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation
Mingzhe Han, Jiahao Liu, Dongsheng Li +5
Federated recommendation enables collaborative model training while keeping user interaction data on local clients. A central problem in federated recommendation is how to aggregat…
ElderBench: Benchmarking Autonomous Mobile Agents for Older Adults
Weide Zhan, Qumu Shaqu, Yuanqing Liu +6
While autonomous mobile agents hold great potential for assisting older adults with smartphone usage, existing GUI benchmarks mainly rely on explicit, goal-oriented instructions an…
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…
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…
Echoes of Norms: Investigating Counterspeech Bots' Influence on Bystanders in Online Communities
Mengyao Wang, Shuai Ma, Nuo Li +4
Counterspeech offers a non-repressive approach to moderate hate speech in online communities. Research has examined how counterspeech chatbots restrain hate speakers and support ta…
Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts
Yaqiong Li, Peng Zhang, Peixu Hou +7
Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on in…
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…
IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference
Guanming Liu, Meng Wu, Peng Zhang +8
Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating sat…
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…
SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking
Yubo Shu, Peng Zhang, Meng Wu +8
Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. H…
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…
"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth
Yaqiong Li, Peng Zhang, Lin Wang +4
Risk perception is subjective, and youth's understanding of toxic content differs from that of adults. Although previous research has conducted extensive studies on toxicity detect…
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…
Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective
Mengyao Wang, Jiayun Wu, Shuai Ma +4
The rapid advancement of AI, including Large Language Models, has propelled autonomous agents forward, accelerating the human-agent teaming (HAT) paradigm to leverage complementary…
YouthCare: Building a Personalized Collaborative Video Censorship Tool to Support Parent-Child Joint Media Engagement
Wenxin Zhao, Fangyu Yu, Peng Zhang +5
To mitigate the negative impacts of online videos on teenagers, existing research and platforms have implemented various parental mediation mechanisms, such as Parent-Child Joint M…
RemiHaven: Integrating "In-Town" and "Out-of-Town" Peers to Provide Personalized Reminiscence Support for Older Drifters
Xuechen Zhang, Changyang He, Peng Zhang +5
With increasing social mobility and an aging society, more older adults in China are migrating to new cities, known as "older drifters." Due to fewer social connections and cultura…
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…
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
Jiafeng Xia, Dongsheng Li, Hansu Gu +4
Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past…
DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship
Yaqiong Li, Peng Zhang, Hansu Gu +5
Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process,…
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-…