The constantly growing offering of online learning materials to students is making it more
difficult to locate specific information from data pools. Personalization systems attempt to …

With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

Precise user and item embedding learning is the key to building a successful recommender
system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item …

Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …

Abstract Graph Neural Networks have been applied in recommender systems to learn the
representation of users and items from a user-item graph. In the state-of-the-art, there are …

A travel recommendation system based on social media activity provides a customized
place of interest to accommodate user-specific needs and preferences. In general, the user's …

Automatic recommendation has become an increasingly relevant problem to industries,
which allows users to discover new items that match their tastes and enables the system to …

Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …

Recommender system (RS) are a type of suggestion to the information overload problem
suffered by user of websites that allow the rating of particular item. The movie RS are one of …

Collaborative Filtering (CF) is one of the most successful approaches for recommender
systems. With the emergence of online social networks, social recommendation has become …