Abstract In the Big Data era, every sector has adapted to technological development service vast amount of information available. this way, each field benefited from improvements over years. The cultural and artistic was no exception, several studies contributed aim interaction between human beings artistic-cultural heritage. scenario, systems able analyze current situation recommend right serv...

Personalized recommenders have proved to be of use as a solution to reduce the information overload ‎problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers ‎suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. ‎Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused...

This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence approach. The system is made available to the user as an adaptive mobile application, which allows a high degree of customization in recommending services and resources according to his/her current position and global profile. A case study applied to the town of Pittsburgh has been analyzed conside...

Social recommendations have been rapidly adopted as important components in social network sites. However, they assume a cooperative relationship between parties involved. This assumption can lead to the creation of privacy issues and new opportunities for privacy infringements. Traditional recommendation techniques fail to address these issues, and as a consequence the development of privacy-a...

Recommender systems have been widely applied to assist user’s decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user’s tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been propose...

Context-aware recommender systems (CARS) go beyond traditional recommender systems, that only consider users’ profiles, by adapting their recommendations also to users’ contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommendation algorithms in different ways. The most effective approaches try to model deviations in ratings ...

Within corporate intranet or on the WWW, a global search engine is the main service used to discover and sort information. Nevertheless, even the most "intelligent" ones have great difficulties to select those targeted to each user specific needs and preferences. We have built a mediated social media named SoMeONe, which helps people to control their information exchanges through trusted relati...

In contrast to traditional recommender systems, context-aware recommender systems (CARS) additionally take context into consideration and try to adapt their recommendations to users’ different contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommenders in different ways. Most of those recommendation algorithms consider model...