The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

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 …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

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 …

Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

Recommender systems widely use implicit feedback such as click data because of its
general availability. Although the presence of clicks signals the users' preference to some …

Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has
been widely used in the recommender system, especially on the mobile Apps. The feed …

Recommender systems deployed in real-world applications can have inherent exposure
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …

In recommender systems, usually the ratings of a user to most items are missing and a
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …