Recommender systems have been widely used in different application domains including
energy-preservation, e-commerce, healthcare, social media, etc. Such applications require …

Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

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 …

Social networks have become very important for networking, communications, and content
sharing. Social networking applications generate a huge amount of data on a daily basis …

With the explosion of social media networks, many modern applications are concerning
about people's connections, which leads to the so-called social computing. An elusive …

Recent years have witnessed the great successes of embedding-based methods in
recommender systems. Despite their decent performance, we argue one potential limitation …

There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …

Collaborative filtering (CF) is one of the most popular techniques for building recommender
systems. To overcome the data sparsity in CF, social recommender systems have emerged …

When users in online social networks make a decision, they are often affected by their
neighbors. Social recommendation models utilize social information to reveal the impact of …

Recommender systems aim to identify relevant items for particular users in large-scale
online applications. The historical rating data of users is a valuable input resource for many …