In this paper we study the synergy between user behavior, context data, and semantic information in order to enable future services to adapt to different situations based on the recommendations of a service-independent recommender. Therefore, we propose a system that delivers context-aware recommendations, which are based on provided feedback, context data, and an ontology-based content categorization scheme. We provide a detailed overview of the specification, a short description of a possible service scenario and a discussion of the results
Recommender systems help users overcome the information overload problem and have been widely used i...
International audienceThis paper provides a service-oriented such solution which explore the ontolog...
Recommender systems have achieved success in a variety of domains, as a means to help users in infor...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
This position paper describes the role ontologies can play in Mobile Context-Aware recommender syste...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
In our work, we have presented two widely used recommendation systems. We have presented a context-a...
Abstract. During the last decade, several recommendation systems have been proposed that help people...
This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual...
International audienceCurrently, we are living in the era of ubiquitous environments, that introduce...
The elements that can be considered under the notion of context in a recommender system are manifold...
Item recommendations calculated by recommender systems mostly in use today, only rely on item conten...
In today's mobile applications, it becomes more and more important to have a broader view on knowled...
Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to...
Recommender systems help users overcome the information overload problem and have been widely used i...
International audienceThis paper provides a service-oriented such solution which explore the ontolog...
Recommender systems have achieved success in a variety of domains, as a means to help users in infor...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
This position paper describes the role ontologies can play in Mobile Context-Aware recommender syste...
Identifying correlations between context data, user behavior, and semantic information can lead to n...
In our work, we have presented two widely used recommendation systems. We have presented a context-a...
Abstract. During the last decade, several recommendation systems have been proposed that help people...
This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual...
International audienceCurrently, we are living in the era of ubiquitous environments, that introduce...
The elements that can be considered under the notion of context in a recommender system are manifold...
Item recommendations calculated by recommender systems mostly in use today, only rely on item conten...
In today's mobile applications, it becomes more and more important to have a broader view on knowled...
Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to...
Recommender systems help users overcome the information overload problem and have been widely used i...
International audienceThis paper provides a service-oriented such solution which explore the ontolog...
Recommender systems have achieved success in a variety of domains, as a means to help users in infor...