Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most o...

Recommender systems are an essential tool to relieve the information overload challenge and play important role in people's daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), issue is whether fair. Unfair not only unethical but also harm long-term interests recommender system itself. As a result, fairness issues have recently attracted increas...

Augmenting personalized recommendations with explanations is believed to improve users’ trust, loyalty, satisfaction, and recommender’s persuasiveness. We present a flexible explanations framework for collaborative filtering recommender systems. Our algorithms utilizes item tags to automatically generate personalized explanations in a natural language format. Given a specific user and a recomme...

One of the modern pillars of collaborative filtering and recommender systems is collection and exploitation of ratings from users. Likert scale is a psychometric quantifier of ratings popular among the electronic commerce sites. In this paper, we consider the tasks of collecting Likert scale ratings of items and of finding the n-k best-rated items, i.e., the n items that are most likely to be t...

It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but sp...

1 Introduction Recommender systems offer benefits to both consumers and firms. For consumers, recommender systems help individuals both become aware of new products as well as select desirable products among myriad choices (Pham & Healey, 2005). For firms, recommender systems have the potential to increase profits by converting browsers into buyers, cross-selling products, and increasing loyalt...

BACKGROUND What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior t...

Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforeh...