Purpose–In the last two decades, a proliferation of business process management (BPM)
modeling languages, standards and software systems has given rise to much confusion and …

In this age of information overload, people use a variety of strategies to make choices about
what to buy, how to spend their leisure time, and even whom to date. Recommender …

With the development of big data and social computing, large-scale group decision making
(LGDM) problems attract much attention and become merging with social networks or …

User-based collaborative filtering systems suggest interesting items to a user relying on
similar-minded people called neighbors. The selection and weighting of these neighbors …

Reputation is a valuable asset in online social lives and it has drawn increased attention.
Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation …

More often than not, it is becoming increasingly evident that the weakest links in the
information-security chain are the people. Due an increase in information security threats, it …

This book describes research performed in the context of trust/distrust propagation and
aggregation, and their use in recommender systems. This is a hot research topic with …

Collaborative filtering (CF) recommender systems have been used in various application
domains to solve the information-overload problem. Recently, trust-based recommender …

Collaborative filtering techniques are commonly used in social networking environments for
proposing user connections or interesting shared resources. While metrics based on access …

Collaborative filtering (CF) is the most commonly applied recommendation system for
personalized services. Since CF systems rely on neighbors as information sources, the …