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 …

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 successful approaches for recommender
systems. With the emergence of online social networks, social recommendation has become …

Social friendship has been shown beneficial for item recommendation for years. However,
existing approaches mostly incorporate social friendship into recommender systems by …

Social recommender systems have attracted a lot of attention from academia and industry.
On social media, users' ratings and reviews can be observed by all users, and have implicit …

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 …

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

With the prevalence of online social platforms, social recommendation has emerged as a
promising direction that leverages the social network among users to enhance …

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 …