Jiawei Chen

Distilling Holistic Knowledge with Graph Neural Networks

2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual knowledge and the …

Neighboring Backdoor Attacks on Graph Convolutional Network

2022 · arXiv (Cornell University)

Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success …

Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Recommender system usually suffers from severepopularity bias— the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users’conformityto the group, which deviates from reflecting …

Spatiotemporal Multiscale Correlation Embedding With Process Variable Reorder for Industrial Soft Sensing

2023 · IEEE Transactions on Instrumentation and Measurement

Soft sensor techniques have been extensively utilised for predicting key variables in the process industry. To extract fine-grained and holistic features for efficient prediction, proper modeling strategies for multiscale spatial and temporal correlations are heavily …

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

2025

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation …

Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems

2025 · arXiv (Cornell University)

Recent years have witnessed extensive exploration of Large Language Models (LLMs) on the field of Recommender Systems (RS). There are currently two commonly used strategies to enable LLMs to have recommendation capabilities: 1) The "Guidance-Only" …

Bias and Debias in Recommender System: A Survey and Future Directions

2022 · ACM Transactions on Information Systems

While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior …

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

2021

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user …

AutoDebias: Learning to Debias for Recommendation

2021

Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned …

KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on …