A hybrid recommendation framework exploiting linked open data & graph-based features

In this article we propose a hybrid recommendation framework based on classification algorithms as Random Forests and Naive Bayes. We fed the framework with several heterogeneous groups of features, and we investigate to what extent features gathered from the Linked Open Data (LOD) cloud (as the genre of a movie or the writer of a book)) as well as graph-based features calculated on the ground of the tripartite representation connecting users, items and properties in the LOD cloud impact on the overall accuracy of the recommendations. In the experimental session we evaluate the effectiveness of our framework on varying of different groups of features, an results show that both LOD-based and graph-based features positively affect the overall performance of the algorithm, especially in highly sparse recommendation scenarios.