GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Expert Systems with Applications: An International Journal
The adaptive web
A Proactive Personalized Mobile News Recommendation System
DESE '10 Proceedings of the 2010 Developments in E-systems Engineering
Adaptation for enriching services taking into account mobile contextual features
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Personalized news recommendation: a review and an experimental investigation
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Social media-driven news personalization
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Graph Databases
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Thanks to new information technologies, users can access a large amount and variety of news stories, anytime and anywhere. Nonetheless, current mechanisms for news dissemination do not properly assist users in spotting news articles that could be potentially interesting for the particular user. Commercial applications are developing solutions to address this problem; however, in academic environments, outdated and inefficient practices are still used. Given these reasons, this paper proposes SADINA (acronym in Spanish of Sistema Adaptativo para la Divulgación Institucional de Noticias Académicas, Adaptive System for Academic News Dissemination, in English). SADINA considers information on new stories (i.e., keywords, publication date), users (i.e., type of user) and context (i.e., access device) in order to provide each user with an ordered set of news items according to the user's individual interests.