Bryan Hooi

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

2016

Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, …

FairJudge: Trustworthy User Prediction in Rating Platforms

2017 · arXiv (Cornell University)

Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify …

Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

2023 · arXiv (Cornell University)

Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel and …

Primacy Effect of ChatGPT

2023

Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the candidate …

Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection

2024 · arXiv (Cornell University)

Anomaly detection (AD) is a machine learning task that identifies anomalies by learning patterns from normal training data. In many real-world scenarios, anomalies vary in severity, from minor anomalies with little risk to severe abnormalities …

Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning

2025 · arXiv (Cornell University)

Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness. This trade-off arises because IFT steers LLMs to generate responses containing long-tail knowledge that was not well covered …

DRS: Deep Question Reformulation With Structured Output

2025

Question answering represents a core capability of large language models (LLMs).However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions that the text itself cannot answer due to insufficient understanding of the underlying …