In many applications of session-based recommendation, social networks are usually
available. Since users' interests are influenced by their friends, recommender systems can …

The task of Session-based Social Recommendation (SSR) aims to utilize the social
networks to make recommendations in session-based scenarios. Existing SSR methods …

Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …

Recommender systems are designed to help users find matching items from plenty of
candidates in online platforms. In many online platforms, such as Yelp and Epinions, users' …

Recommendation system utilizes user-item interactions and information on user's attributes
to infer the user's interests and use them to make recommendations for the user. Graph …

Session-based recommendation tries to make use of anonymous session data to deliver
high-quality recommendations under the condition that user profiles and the complete …

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 …

Streaming session-based recommendation (SSR) is a challenging task that requires the
recommender system to do the session-based recommendation (SR) in the streaming …

Collaborative filtering (CF) is one of the most popular techniques for building recommender
systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by …

Session-based recommendation aims to predict the next item that is most likely to be clicked
by an anonymous user, based on his/her clicking sequence within one visit. It becomes an …