Hansu Gu
YOU?
Author Swipe
View article: Unbiased Collaborative Filtering with Fair Sampling
Unbiased Collaborative Filtering with Fair Sampling Open
View article: Improving LLM-powered Recommendations with Personalized Information
Improving LLM-powered Recommendations with Personalized Information Open
View article: FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation Open
View article: AOTree: Aspect Order Tree-Based Model for Explainable Recommendation
AOTree: Aspect Order Tree-Based Model for Explainable Recommendation Open
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews…
View article: EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association Open
Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-commerce, customers increasingly seek LLM-based assistants to …
View article: Frequency-aware Graph Signal Processing for Collaborative Filtering
Frequency-aware Graph Signal Processing for Collaborative Filtering Open
View article: Filtering Discomforting Recommendations with Large Language Models
Filtering Discomforting Recommendations with Large Language Models Open
View article: AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents Open
A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications. Yet, traditional A/B testing remains constrained by its dependence on the large-scale and live traffic of human participant…
View article: RemiHaven: Integrating "In-Town" and "Out-of-Town" Peers to Provide Personalized Reminiscence Support for Older Drifters
RemiHaven: Integrating "In-Town" and "Out-of-Town" Peers to Provide Personalized Reminiscence Support for Older Drifters Open
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 cultural adaptation challenges, they face negative emotions such a…
View article: YouthCare: Building a Personalized Collaborative Video Censorship Tool to Support Parent-Child Joint Media Engagement
YouthCare: Building a Personalized Collaborative Video Censorship Tool to Support Parent-Child Joint Media Engagement Open
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 Media Engagement (JME). However, JME generally relies heavil…
View article: Improving LLM-powered Recommendations with Personalized Information
Improving LLM-powered Recommendations with Personalized Information Open
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 called CoT-Rec, which integrates two key Chain-of-Thought …
View article: Unbiased Collaborative Filtering with Fair Sampling
Unbiased Collaborative Filtering with Fair Sampling Open
Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular items. In this paper, we demonstrate that popularity bias a…
View article: AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations Open
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 often exhibit cross-domain characteristics and are influen…
View article: UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design
UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design Open
Usability testing is a fundamental yet challenging (e.g., inflexible to iterate the study design flaws and hard to recruit study participants) research method for user experience (UX) researchers to evaluate a web design. Recent advances i…
View article: EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association Open
View article: Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data Open
View article: Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation Open
Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to…
View article: Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data Open
In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. However, the large size and high computation demands of LLMs limit their practicality in many applicat…
View article: Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI
Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI Open
View article: DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship
DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship Open
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, wherein detection is just an initial task and a user can ha…
View article: Filtering Discomforting Recommendations with Large Language Models
Filtering Discomforting Recommendations with Large Language Models Open
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effec…
View article: GraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering
GraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering Open
Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering tasks due to their ability to extract powerful structural features. However, combining the graph features extracted from user-item interactions and aux…
View article: AOTree: Aspect Order Tree-based Model for Explainable Recommendation
AOTree: Aspect Order Tree-based Model for Explainable Recommendation Open
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews…
View article: Frequency-aware Graph Signal Processing for Collaborative Filtering
Frequency-aware Graph Signal Processing for Collaborative Filtering Open
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 importance of various interactions that reflect unique user/i…
View article: Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction Open
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 prediction performance of CTR models. However, existing metho…
View article: A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction Open
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 prediction is mainly improved by designing practical featu…
View article: RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents
RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents Open
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 on individual preferences. Despite their importance, challen…
View article: AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation Open
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them.…
View article: Recommendation Unlearning via Matrix Correction
Recommendation Unlearning via Matrix Correction Open
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), security (e.g., malicious data) and utility (e.g., toxic d…
View article: Simulating News Recommendation Ecosystem for Fun and Profit
Simulating News Recommendation Ecosystem for Fun and Profit Open
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 platforms, the existing literature is limited in understanding …