Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …

Personalized news recommendation aims to recommend news articles to customers, by
exploiting the personal preferences and short-term reading interest of users. A practical …

Online news recommender systems aim to make personalized recommendations according
to user preferences, which require modeling users' short-term reading interest. However …

Session-based recommendation aims at predicting the next item given a sequence of
previous items consumed in the session, eg, on e-commerce or multimedia streaming …

In this work, we address the problem of providing job recommendations in an online session
setting, in which we do not have full user histories. We propose a recommendation …

Session-based recommendation (SR) predicts the next items from a sequence of previous
items consumed by an anonymous user. Most existing SR models focus only on modeling …

E-commerce applications rely heavily on session-based recommendation algorithms to
improve the shopping experience of their customers. Recent progress in session-based …

A Session-based recommender system (SBRS) captures the dynamic behavior of a user to
provide recommendations for the next item in the current session. On providing the user's …

Recommender systems currently play a significant role in many areas related to the
processing of large amounts of information, such as online stores and cinemas, services for …