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

The growing popularity of social networks and their easy acceptance of new users have the
unintended consequence of fostering an environment where anonymous users can act in …

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

User-based collaborative filtering, a widely used nearest neighbour-based recommendation
technique, predicts an item's rating by aggregating its ratings from similar users. User …

Providing high quality recommendations is important for e-commerce systems to assist users
in making effective selection decisions from a plethora of choices. Collaborative filtering is a …

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

Trust is one source of information that has been widely adopted to personalize online
services for users, such as in product recommendations. However, trust information is …

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 …

Personalized recommendation for online service systems aims to predict potential demand
by analysing user preference. User preference can be inferred from heterogeneous implicit …

Social recommendation, which aims to exploit social information to improve the quality of a
recommender system, has attracted an increasing amount of attention in recent years. A …