In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

Looking for information about symptoms and illnesses on the Internet is common and often
serves useful purposes. However, a number of people who are overly distressed or anxious …

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 …

As language models become more powerful, training and evaluation are increasingly
bottlenecked by the data and metrics used for a particular task. For example, summarization …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

We present a framework for quantifying and mitigating algorithmic bias in mechanisms
designed for ranking individuals, typically used as part of web-scale search and …

Recommender systems rely on user behavior data like ratings and clicks to build
personalization model. However, the collected data is observational rather than …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by
experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate …

Metric learning algorithms produce distance metrics that capture the important relationships
among data. In this work, we study the connection between metric learning and collaborative …