Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two si...

This paper explores the evaluation issues of recommender systems particularly from users’ perspective. We first show results of literature surveys on human psychological decision theory and trust building in online environments. Based on the results, we propose an evaluation framework aimed at assessing a recommender’s practical ability in providing decision support benefits to end-users from v...

This paper focuses on stability of recommendation algorithms, which measures the consistency of recommender system predictions. Stability is a desired property of recommender systems and has important implications on users' trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms suffer from high degree of instability. In this study we pro...

Mobile applications increasingly use personalization, to maximize the relevance of displayed content and to simplify the user interaction. However, disclosing the personal information that is required for personalization makes many users feel uncomfortable. It is believed that empowering users to make their own privacy decisions by giving them adequate notifications and disclosure controls woul...

I nternet applications have evolved from centralized and private computing platforms to distributed and collaborative networked computing systems. Collaboration is a fundamental Internet computing capability, and collaborative computing represents a significant step toward the continued promotion of openness and social intelligence as Internet systems and applications evolve. However, trust and...