65 citations · 232 across the 23 of their papers we have counts for
23 papers
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…
Role Identification based Method for Cyberbullying Analysis in Social Edge Computing
Runyu Wang, Tun Lu, Peng Zhang +1
Over the past few years, many efforts have been dedicated to studying cyberbullying in social edge computing devices, and most of them focus on three roles: victims, perpetrators,…
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…
Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization
Shitong Duan, Xiaoyuan Yi, Peng Zhang +5
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduce…
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…
Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground
Zhenyu Xu, Hailin Xu, Zhouyang Lu +11
Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical s…
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Yuxuan Lu, Bingsheng Yao, Shao Zhang +5
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domai…
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…
Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning
Shitong Duan, Xiaoyuan Yi, Peng Zhang +3
Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical conten…
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…
RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents
Yubo Shu, Haonan Zhang, Hansu Gu +4
The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in Human-Computer Interaction (HCI) by tailoring content based o…
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),…
Simulating News Recommendation Ecosystem for Fun and Profit
Guangping Zhang, Dongsheng Li, Hansu Gu +3
Understanding the evolution of online news communities is essential for designing more effective news recommender systems. However, due to the lack of appropriate datasets and plat…
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…
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Fangye Wang, Yingxu Wang, Dongsheng Li +4
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representat…
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…
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
Fangye Wang, Yingxu Wang, Dongsheng Li +4
CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation…
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices
Yingying Zhao, Yuhu Chang, Yutian Lu +8
Emotion recognition in smart eyewear devices is highly valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye…
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline
Yingying Zhao, Mingzhi Dong, Yujiang Wang +7
Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and relian…
MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture
Yuhu Chang, Yingying Zhao, Mingzhi Dong +7
This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX cap…