Executing, Comparing, and Reusing Linked Data-Based Recommendation Algorithms With the Allied Framework

International audienceData published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset

Semantics-aware Recommender Systems exploiting Linked Open Data and graph-based features

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