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

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 …

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

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 …

Recommender systems are crucial to alleviate the information overload problem in online
worlds. Most of the modern recommender systems capture users' preference towards items …

Social influence is essential to social recommendation. Current influence-based social
recommendation focuses on the explicit influence on observed social links. However, in real …

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 …

This paper is concerned with how to make efficient use of social information to improve
recommendations. Most existing social recommender systems assume people share similar …

Generating recommendations based on user-item interactions and user-user social relations
is a common use case in web-based systems. These connections can be naturally …

Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models …