This paper examines the problem of social collaborative filtering (CF) to recommend items of
interest to users in a social network setting. Unlike standard CF algorithms using relatively …

Content recommendation in social networks poses the complex problem of learning user
preferences from a rich and complex set of interactions (eg, likes, comments and tags for …

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 …

This paper addresses the issue of social recommendation based on collaborative filtering
(CF) algorithms. Social recommendation emphasizes utilizing various attributes information …

Recommendation plays an increasingly important role in our daily lives. Recommender
systems automatically suggest to a user items that might be of interest to her. Recent studies …

Collaborative Filtering (CF) based recommender systems often suffer from the sparsity
problem, particularly for new and inactive users when they use the system. The emerging …

Social recommendation, which aims to systematically leverage the social relationships
between users as well as their past behaviors for automatic recommendation, attract much …

Collaborative filtering (CF) technique plays an important role in generating personalized
recommendations, but its performance is challenged by the problems of data sparsity and …

Recommender systems (RS) are found in many online applications where users are
exposed to huge sets of items. The goal of recommender systems is to provide the users …

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 …