Fedelucio Narducci

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As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of(More)
This paper provides an overview of the work done in the Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in(More)
The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing(More)
This paper presents MyMusic, a system that exploits social media sources for generating personalized music playlists. This work is based on the idea that information extracted from social networks, such as Facebook and Last.fm, might be effectively exploited for personalization tasks. Indeed, information related to music preferences of users can be easily(More)
The large diffusion of e-gov initiatives is increasing the attention of public administrations towards the Open Data initiative. The adoption of open data in the e-gov domain produces different advantages in terms of more transparent government, development of better public services, economic growth and social value. However, the process of data opening(More)
A content-based recommender system suggests items similar to those previously liked by a user, therefore the recommendation process consists of matching up the features stored in a user profile with those of a content object (item). Usually a content-based user profile stores keywords that are more meaningful for that specific user. Common-sense knowledge(More)
This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a knowledge-based algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with information coming from both a distributional semantic model and usage frequency of(More)
In this work we present a semantic recommender system able to suggest doctors and hospitals that best fit a specific patient profile. The recommender system is the core component of the social network named HealthNet (HN). The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospitals(More)
The main contribution of this work is the comparison of different techniques for representing user preferences extracted by analyzing data gathered from social networks, with the aim of constructing more transparent (human-readable) and serendipitous user profiles. We compared two different user models representations: one based on keywords and one(More)
The continuous growth of collaborative platforms we are recently witnessing made possible the passage from an ‘elitary’ Web, written by few and read by many, towards the so-called Web 2.0, a more ‘user-centric’ vision, where users become active contributors in Web dynamics. In this context, collaborative tagging systems are rapidly emerging: in these(More)