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 …

An ongoing project explores the extent to which artificial intelligence (AI), specifically in the
areas of natural language processing and semantic reasoning, can be exploited to facilitate …

In this paper, we introduce a large scale multi-objective ranking system for recommending
what video to watch next on an industrial video sharing platform. The system faces many …

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

Rankings of people and items are at the heart of selection-making, match-making, and
recommender systems, ranging from employment sites to sharing economy platforms. As …

Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …

Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …

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 …

We introduce the concept of expected exposure as the average attention ranked items
receive from users over repeated samples of the same query. Furthermore, we advocate for …

Prior research on exposure fairness in the context of recommender systems has focused
mostly on disparities in the exposure of individual or groups of items to individual users of …