Methods for bandit learning from user interactions often require a model of the reward a
certain context-action pair will yield–for example, the probability of a click on a …

Modern recommender systems are often modelled under the sequential decision-making
paradigm, where the system decides which recommendations to show in order to maximise …

The contextual bandit paradigm provides a general framework for decision-making under
uncertainty. It is theoretically well-defined and well-studied, and many personalisation use …

Conventional approaches to recommendation often do not explicitly take into account
information on previously shown recommendations and their recorded responses. One …

Approaches to recommendation are typically evaluated in one of two ways:(1) via a
(simulated) online experiment, often seen as the gold standard, or (2) via some offline …

In real-world recommender model deployments, the models are typically retrained and
deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to …

Recommender Systems are especially challenging for marketplaces since they must
maximize user satisfaction while maintaining the healthiness and fairness of such …

Recommender systems are information retrieval applications that provide users with
algorithmic recommendations, in order to assist decision-making when sufficient knowledge …

Recommender systems have seen a rapid rise of application in various industries, with
several services now being implemented online. Over the years, various authors have been …

The objective of this tutorial is to give a structured overview of the conceptual frameworks
behind current state-of-the-art recommender systems, explain their underlying assumptions …