40 citations · 84 across the 10 of their papers we have counts for
11 papers
PriCoRec: A Privacy-Aware Cloud-Device Collaborative Framework for Ad Recommendation under Feature Constraints
Dairui Liu, Zhongyi Lu, Jitao Lu +7
Privacy regulations increasingly restrict cloud processing of sensitive user data (e.g., age, gender), hindering traditional cloud-only recommendation models. To mitigate this chal…
RAMP: Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways
Dairui Liu, Zhongyi Lu, Roger Zhe Li +11
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various f…
Thinking with Visual Abstract: Enhancing Multimodal Reasoning via Visual Abstraction
Dairu Liu, Ziyue Wang, Minyuan Ruan +4
Images usually convey richer detail than text, but often include redundant information, which potentially downgrades multimodal reasoning performance. When faced with lengthy or co…
Transformers4NewsRec: A Transformer-based News Recommendation Framework
Dairui Liu, Honghui Du, Boming Yang +5
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these mod…
RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models
Dairui Liu, Boming Yang, Honghui Du +5
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user inter…
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Fan Gao, Hang Jiang, Rui Yang +7
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and u…
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Boming Yang, Dairui Liu, Toyotaro Suzumura +2
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using ad…
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
Xuefei Jiang, Dairui Liu, Ruihai Dong
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the c…
Topic-Centric Explanations for News Recommendation
Dairui Liu, Derek Greene, Irene Li +2
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated con…
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Dairui Liu, Derek Greene, Ruihai Dong
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep le…
BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
Dairui Liu, Jingxiang Sun, Shen Wang
With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions. This type of appl…