The creation of novel recommendation algorithms for social networks is currently struggling with the volume of available data originating in such environments. Given that social networks can be modeled as graphs, a distributed graph-oriented support to exploit the computing capabilities of clusters arises as a necessity. In this thesis, a platform for graph storage and processing named Graphly is proposed along with GraphRec, an API for easy specification of recommendation algorithms. Graphly and GraphRec hide distributed programming concerns from the user while still allowing fine-tuning of the remote execution. For example, users may customize an algorithm execution using job distribution strategies, without modifying the original code. GraphRec also simplifies the design of graph-based recommender systems by implementing well-known algorithms as "primitives'' that can be reused.
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