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 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 …

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 …

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

The tremendous growth in the amount of attention and users, on social networking sites
(SNSs), has led to information overload and that adds to the difficulty of making accurate …

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

Collaborative filtering is a widely used technique for providing personalized
recommendations to users. However, traditional collaborative filtering methods fail to …

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 …

Social-based recommenders seek to exploit the mechanisms of homophily and influence
observed in social networks in order to provide more accurate recommendations. The way …