Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Collaborative filtering recommender systems (CFRSs) have already been proved effective to
cope with the information overload problem since they merged in the past two decades …

Collaborative filtering suffers from the problems of data sparsity and cold start, which
dramatically degrade recommendation performance. To help resolve these issues, we …

Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …

We propose TrustSVD, a trust-based matrix factorization technique for recommendations.
TrustSVD integrates multiple information sources into the recommendation model in order to …

Today, people frequently communicate through interactions and exchange knowledge over
the social web in various formats. Social connections have been substantially improved by …

Recommender systems (RSs) are programs that apply knowledge discovery techniques to
make personalized recommendations for user's information on the web. In online sharing …

Recommender systems are information filtering tools that aspire to predict the rating for
users and items, predominantly from big data to recommend their likes. Movie …

Purpose This paper aims to identify, evaluate and integrate the findings of all relevant and
high-quality individual studies addressing one or more research questions about …

The importance of recommendation systems for business applications has led to extensive
research efforts to improve the recommendations accuracy as well as to reduce the sparsity …