65 citations · 177 across the 30 of their papers we have counts for
32 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…
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…
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…
SparkTales: Facilitating Cross-Language Collaborative Storytelling through Coordinator-AI Collaboration
Wenxin Zhao, Peng Zhang, Hansu Gu +6
Cross-language collaborative storytelling plays a vital role in children's language learning and cultural development, fostering both expressive ability and intercultural awareness…
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…
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-…
GraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering
Jiafeng Xia, Dongsheng Li, Hansu Gu +2
Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering tasks due to their ability to extract powerful structural features. However, combining the g…
AOTree: Aspect Order Tree-based Model for Explainable Recommendation
Wenxin Zhao, Peng Zhang, Hansu Gu +3
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recomme…
Frequency-aware Graph Signal Processing for Collaborative Filtering
Jiafeng Xia, Dongsheng Li, Hansu Gu +4
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the im…
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
Fangye Wang, Hansu Gu, Dongsheng Li +3
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the pr…
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
Fangye Wang, Hansu Gu, Dongsheng Li +4
Click-through rate (CTR) prediction is widely used in academia and industry. Most CTR tasks fall into a feature embedding \& feature interaction paradigm, where the accuracy of CTR…
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…