Xuanhui Wang

Learning to Rank with Selection Bias in Personal Search

2016

Click-through data has proven to be a critical resource for improving search ranking quality. Though a large amount of click data can be easily collected by search engines, various biases make it difficult to fully …

TF-Ranking

2019

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization …

Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I.

2024

The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- can generate relevance …

Position Bias Estimation for Unbiased Learning to Rank in Personal Search

2018

A well-known challenge in learning from click data is its inherent bias and most notably position bias. Traditional click models aim to extract the ‹query, document› relevance and the estimated bias is usually discarded after …