29 citations · 31 across the 13 of their papers we have counts for
14 papers
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Miaomiao Cai, Min Hou, Lei Chen +4
Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based…
Understanding Embedding Scaling in Collaborative Filtering
Yicheng He, Zhou Kaiyu, Haoyue Bai +2
Scaling recommendation models into large recommendation models has become one of the most widely discussed topics. Recent efforts focus on components beyond the scaling embedding d…
Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation
Haoyue Bai, Haoyu Wang, Shengyu Chen +5
Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…
Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO Storage
Haoyue Bai, Guodong Chen, Wangyang Ying +7
Geological CO2 storage (GCS) involves injecting captured CO2 into deep subsurface formations to support climate goals. The effective management of GCS relies on adaptive injection…
Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback
Wangyang Ying, Haoyue Bai, Nanxu Gong +4
The data-to-equation (Data2Eqn) task aims to discover interpretable mathematical equations that map observed values to labels, offering physical insights and broad applicability ac…
Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation
Nanxu Gong, Zijun Li, Sixun Dong +4
Feature Transformation (FT) crafts new features from original ones via mathematical operations to enhance dataset expressiveness for downstream models. However, existing FT methods…
Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories
Nanxu Gong, Sixun Dong, Haoyue Bai +3
As a widely-used and practical tool, feature engineering transforms raw data into discriminative features to advance AI model performance. However, existing methods usually apply f…
Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming
Nanxu Gong, Xinyuan Wang, Wangyang Ying +4
Feature transformation involves generating a new set of features from the original dataset to enhance the data's utility. In certain domains like material performance screening, di…
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
Dongjie Wang, Yanyong Huang, Wangyang Ying +11
Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric…
Topology-aware Reinforcement Feature Space Reconstruction for Graph Data
Wangyang Ying, Haoyue Bai, Kunpeng Liu +1
Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data…
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Miaomiao Cai, Lei Chen, Yifan Wang +5
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to…
It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Zhuangzhuang He, Zihan Wang, Yonghui Yang +2
Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, and self-supervised…
Double Correction Framework for Denoising Recommendation
Zhuangzhuang He, Yifan Wang, Yonghui Yang +6
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in…
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Haoyue Bai, Le Wu, Min Hou +5
Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge numb…