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 …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Predicting the next interaction of a short-term interaction session is a challenging task in
session-based recommendation. Almost all existing works rely on item transition patterns …

The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Deep learning (DL) techniques have been widely used in recommender systems for user
modeling and matching function learning based on historical interaction matrix. However …

Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this
scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential …