In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

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 …

Industrial recommender systems deal with extremely large action spaces--many millions of
items to recommend. Moreover, they need to serve billions of users, who are unique at any …

Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

In many real-world reinforcement learning applications, access to the environment is limited
to a fixed dataset, instead of direct (online) interaction with the environment. When using this …

Two-sided marketplaces are platforms that have customers not only on the demand side (eg
users), but also on the supply side (eg retailer, artists). While traditional recommender …

When a user has watched, say, 70 romance movies and 30 action movies, then it is
reasonable to expect the personalized list of recommended movies to be comprised of about …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …