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 considering various users (with different profiles as visitors, students, professors) and an experimental campaign has been conducted obtaining interesting result
Traditional recommender systems provide personal suggestions based on the user’s preferences, withou...
Recommender systems help online users find relevant content by suggesting information of potential i...
Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (accep...
This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence ap...
Context-aware recommender systems (CARSs) gradually play a crucial role in modern information system...
Recommender systems are software tools and techniques providing suggestions and recommendations for ...
For classical domains, such as movies, recommender systems have proven their usefulness. But recomme...
Abstract. In this demo paper we present a novel context-aware mo-bile recommender system for places ...
Abstract — Recommender Systems have been/are being researched and deployed extensively in various di...
Intelligent data handling techniques are beneficial for users; to store, process, analyze and access...
Abstract. In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender sy...
Due to the rapid increase of social network resources and services, Internet users are now overwhelm...
Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) commu...
In our work, we have presented two widely used recommendation systems. We have presented a context-a...
Recommender systems have dramatically changed the way we consume content. Internet applications rely...
Traditional recommender systems provide personal suggestions based on the user’s preferences, withou...
Recommender systems help online users find relevant content by suggesting information of potential i...
Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (accep...
This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence ap...
Context-aware recommender systems (CARSs) gradually play a crucial role in modern information system...
Recommender systems are software tools and techniques providing suggestions and recommendations for ...
For classical domains, such as movies, recommender systems have proven their usefulness. But recomme...
Abstract. In this demo paper we present a novel context-aware mo-bile recommender system for places ...
Abstract — Recommender Systems have been/are being researched and deployed extensively in various di...
Intelligent data handling techniques are beneficial for users; to store, process, analyze and access...
Abstract. In this paper we present STS (South Tyrol Suggests), a context-aware mobile recommender sy...
Due to the rapid increase of social network resources and services, Internet users are now overwhelm...
Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) commu...
In our work, we have presented two widely used recommendation systems. We have presented a context-a...
Recommender systems have dramatically changed the way we consume content. Internet applications rely...
Traditional recommender systems provide personal suggestions based on the user’s preferences, withou...
Recommender systems help online users find relevant content by suggesting information of potential i...
Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (accep...