Marc Najork

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 …

Learning Effective Embeddings for Machine Generated Emails with Applications to Email Category Prediction

2018

Machine generated business-to-consumer (B2C) emails such as receipts, newsletters, and promotions constitute a large portion of users' inboxes today. These emails reflect the users' interests and often are sequentially correlated, e.g., users interested in relocating …

STRUM: Extractive Aspect-Based Contrastive Summarization

2023

Comparative decisions, such as picking between two cars or deciding between two hiking trails, require the users to visit multiple webpages and contrast the choices along relevant aspects. Given the impressive capabilities of pre-trained large …

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 …