Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

Recommender systems have become one of the main tools for personalized content filtering
in the educational domain. Those who support teaching and learning activities, particularly …

Personalization is one of the key applications in machine learning with widespread usage
across e-commerce, entertainment, production, healthcare and many other industries. While …

Recommender systems have become increasingly accurate in suggesting content to users,
resulting in users primarily consuming content through recommendations. This can cause …

Nowadays, Recommender Systems have played a crucial role in several entertainment
scenarios by making personalised recommendations and guiding the entire users' journey …

Personalized recommender systems suffuse modern life, sha** what media we read and
what products we consume. Algorithms powering such systems tend to consist of supervised …

Bandit learning has been an increasingly popular design choice for recommender system.
Despite the strong interest in bandit learning from the community, there remains multiple …

Driven by the need to capture users' evolving interests and optimize their long-term
experiences, more and more recommender systems have started to model recommendation …

Interactive Recommender Systems have played a crucial role in distinct entertainment
domains through a Contextual Bandit model. Despite the current advances, their …

There is strong interest in leveraging reinforcement learning (RL) for information retrieval
(IR) applications including search, recommendation, and advertising. Just in 2020, the term" …