Anexos

Managing the current data deluge is a great challenge for users. Emails are constantly arriving, notifications of tweets and RSS feeds keep popping out, newspapers and blogs of different types publish potentially-relevant news every day, etc. If a user wants to keep track of certain topics in an efficient way, a careful filtering is needed in order to keep the number of items to review manageable, as otherwise the user may finally give up or just perform some random or casual reading. Automated tools can help the user to perform this initial selection, and thus to minimize the feeling of being overwhelmed that the user may experience. In this short paper, we present our ongoing work for the development of DodoAid, a recommender of digital objects that attempts to alleviate the current user’s overload when he/she wants to follow information about certain topics. Beyond the application of information retrieval and text mining techniques, it can also apply techniques from the field of recommender systems to suggest items that not only fit topics of interest for the user but are also expected to be valuable according to the individual user’s preferences, which can be learnt automatically in an implicit way.