Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new field has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scen...

Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...

In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like kMeans and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust co...

Recent technological trends such as cloud computing, wireless communication, and wireless sensor networks provide a strong infrastructure and offer a true enabler for health information technology services over the Internet. This system is based on the cloud computing environment, integrating mobile communication technology, context-aware technology, and wireless sensor networks to build a mobi...

Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately so far with regard to this. In this work we are proposing and testing a scheme that uses the exis...

Abstract Molecular nature of cancer is the foundation systematic studies genomes, providing exceptional insights and allowing treatments advancement in clinic. We combine techniques image processing for feature enhancement recommender systems proposing a personalized ranking drugs. use database containing drug sensitivity data more than 310.000 IC50, describing response 300 anticancer drugs acr...

Although taste and trust are concepts on clearly distinct ontological levels, they are strongly interrelated in several contexts. For instance, when assessing trust, e.g. through a trust network, it is important to understand the role that personal taste plays in order to correctly interpret potential value dependent trust recommendations and conclusions, in order to provide a sound basis for d...

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based ...