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...

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science...

When recommending personalized top-k items to users, how can we recommend them diversely while satisfying users’ needs? Aggregately diversified recommender systems aim a variety of across whole users without sacrificing the recommendation accuracy. They increase exposure opportunities various items, which in turn potential revenue sellers as well user satisfaction. However, it is challenging ta...

Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for a given item. In this paper we propose an adaptation of the search algorithms proposed in [14, 1] to the tag recommendation problem. Our algorithm, called STRec, provides networkaware recommendations based on proximity measures computed on-the-fly in the network. STRec uses a bounded s...

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...