With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

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 …

This paper proposes K-NRM, a kernel based neural model for document ranking. Given a
query and a set of documents, K-NRM uses a translation matrix that models word-level …

Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …

This paper presents\textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model
that models n-gram soft matches for ad-hoc search. Instead of exact matching query and …

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 …

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 …

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 …

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 …