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

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 …

Recommender Systems (RS) have proven to be effective tools to help users overcome
information overload, and significant advances have been made in the field over the past …

We propose Meta-Prod2vec, a novel method to compute item similarities for
recommendation that leverages existing item metadata. Such scenarios are frequently …

Skip-gram with negative sampling, a popular variant of Word2vec originally designed and
tuned to create word embeddings for Natural Language Processing, has been used to …

Traditional music recommender systems are mainly based on users' interactions, which limit
their performance. Particularly, various kinds of content information, such as metadata and …

Session-based recommendation focuses on the prediction of user actions based on
anonymous sessions and is a necessary method in the lack of user historical data. However …

Session-based recommendation (SBR) aims to predict a user's next clicked item based on
an anonymous yet short interaction sequence. Previous SBR models, which rely only on the …

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 (RS) aim to provide personalized suggestions of items for users
against consumer over-choice. Although extensive research has been conducted to address …