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Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this paper we introduce the concept of personal channel, on which Personalized EPGs are grounded, that provides users with potentially interesting programs and videos, by exploiting program genres (documentary, sports, ...) and short textual descriptions of programs to find and categorize them. We investigate the problem of adopting appropriate algorithms for TV-program classification and retrieval, in the context of building personal channels, which is harder than a classical retrieval or classification task because of the short text available. The approach proposed to overcome this problem is the adoption of a new feature generation technique that enriches the textual program descriptions with additional features extracted from Wikipedia. Results of the experiments show that our approach actually improves the retrieval performance, while a limited positive effect is observed on classification accuracy. [ABSTRACT FROM AUTHOR]

The recent spread of Linked Open Data (LOD) fueled the research in the area of Recommender Systems, since the (semantic) data points available in the LOD cloud can be exploited to improve the performance of recommendation algorithms by enriching item representations with new and relevant features. In this article we investigate the impact of the features gathered from the LOD cloud on a hybrid recommendation framework based on three classification algorithms, Random Forests, Naïve Bayes and Logistic Regression. Specifically, we extend the representation of the items by introducing two new types of features: LOD-based features , structured data extracted from the LOD cloud, as the genre of a movie or the writer of a book, and graph-based features , computed on the ground of the topological characteristics of both the bipartite graph-based representation connecting users and items, and the tripartite representation connecting users, items and properties in the LOD cloud. In the experimental session we assess the effectiveness of these novel features; results show that the use of information coming from the LOD cloud could improve the overall accuracy of our recommendation framework. Finally, our approach outperform several state-of-the-art recommendation techniques, thus confirming the insights behind this research. [ABSTRACT FROM AUTHOR]

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research. [ABSTRACT FROM AUTHOR]