A kind of algorithms or frameworks designed to recommend items of interest for an end user. These items can belong to different categories or types, e.g. songs, places, news, books, films, events, etc.
Published in Chapter:
Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework
Cristhian Figueroa (Universidad Antonio Nariño, Colombia), Iacopo Vagliano (Leibniz Information Centre for Economics, Germany), Oscar Rodríguez Rocha (Université Côte d'Azur, France), Marco Torchiano (Politecnico di Torino, Italy), Catherine Faron Zucker (Université Nice Sophia Antipolis, France), Juan Carlos Corrales (Universidad del Cauca, Colombia), and Maurizio Morisio (Politecnico di Torino, Italy)
Copyright: © 2019
|Pages: 30
DOI: 10.4018/978-1-5225-7186-5.ch002
Abstract
Data published on the web following the principles of linked data 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.