Abstract
According to IBM statistics, the internet generates 2.5 trillion items of heterogeneous data on a daily basis. Known as 'big data', this degrades the performance of search engines and reduces their ability to satisfy requests. Filtering systems such as Netflix, ebay, iTunes and others are widely used on the web to select and distribute interesting resources to users. Most of these systems recommend only one kind of resource, which limits the ambitions of their users. In this paper, we propose a hybrid recommendation system that includes a variety of resources (books, films, music, etc.). A similarity process was applied to group users and resources on the basis of appropriate metadata. We have also used a graph data model known as a resource description framework (RDF) to represent the different modules of the system. RDF syntax allows for perfect integration and data exchange via the SPARQL query language. Real datasets are used to perform the experiments, showing promising results in terms of performance and accuracy.
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