Social commerce based on relations is developing rapidly in recent years and the personalized recommender systems make the contribution. Based on the traditional collaborative filtering (CF) algorithm, this study proposes a social recommender systems that combing preference similarity, reputation-based trust and social relations between users. Using the real data from Epinions.com, we compared ...

Recommender systems have evolved during the last few years into useful online tools for assisting the daily e-commerce activities. The majority of recommender systems predict user preferences relating users with similar taste. Prior research has shown that trust networks improve the performance of recommender systems, predominantly using algorithms devised by individual researchers. In this wor...

Current recommender systems using network data focus on a single network, either a trust or a document reference network. However, in a number of applications, several types of networks need to be taken into account. The paper describes an architecture for a recommender system for publications that integrates information from different networks. Relationships in the trust network are modeled in...

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. Userand item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are...

Recommender systems have been successfully used in electronic commerce applications such as recommending books, movies, restaurants and airlines based on users’ past behaviour. More recently, such systems have made inroads into social media, for examples to recommend partners in online dating sites. In our work, we have developed a social behaviour based recommender system within an online comm...