Exponential growth of information generated by social networks requires efficient and
scalable recommendation techniques to produce useful results. Traditional methods have …

Recommender systems with social networks have been well studied in recent years.
However, most of these methods ignore the social contextual information among users and …

Online social platforms have provided a large amount of available information to
recommendation systems. With this intuition, social recommendation systems emerged and …

Recommender systems with social networks (RSSN) have been well studied in recent
works. However, these methods ignore the relationships among items, which may affect the …

With a large amount of complex network data available, most existing recommendation
models consider exploiting rich user social relations for better interest targeting. In these …

Traditional recommender systems often suffer from the problem of data sparsity, because
most users rate only a few of the millions of possible items. With the development of social …

Exponential growth of information generated by online social networks demands effective
recommender systems to give useful results. Traditional techniques become unqualified …

In the real world, when we seek our friends' opinions on various items or events, we request
verbal social recommendations. It has been observed that we often turn to our friends for …

Social recommendation can effectively improve recommendation performance by leveraging
social relationships to alleviate the sparsity of user-item interaction data. Because these …

Recommender systems suggest to users the items that are potentially of their interests, by
mining users' feedback data on items. Social relations provide an independent source of …