Recommender Systems are effective methods that have been developed successfully in e-commerce to provide personal recommendations based on user’s previous preferences. A recommender system assists users in a decision-making process to suggest interesting items from overwhelming flood of data available in the system. Suitably adapting the technology, recommender systems for medical purposes have...

The issue of trust is important in recommender systems. These systems are typically described in terms of perceived reliability of the recommender coupled with a content quality perspective. However, most studies do not address the complete user context and psychological environment of a recommender system. This environment and context are described here by three primary areas of consideration,...

We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called “recommender systems”. Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays, especially with the advent of the Inte...

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...

WHO estimates that the number of people with diabetes will grow 114% by 2030.It declares that, patients have to play a major role to control and therapy of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, diet therapy in particular. In this regard, diets recommender Systems would be helpful. They are techniques and tools which su...