Recent developments with Neural Networks produced models which are capable of encoding graph structured data. The most promising and arguably the most capable of these methods are the Graph Neural Networks (GNNs). This paper considers the GNN for the application to recommender system learning problem. It will be shown that the GNN can generally exploit relational information in such a graph structured domain but experiments also revealed some interesting limitations of the GNN. Experiments were conducted on a relatively large set of real world data from MovieLens. The dataset that has been widely used as a benchmark problem. The careful analysis of these data led to the discovery of some intriguing properties which helped to explain the problems and limitations of the GNN when dealing with this learning problem.
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|Titolo:||Investigations into the application of Graph Neural Networks to large-scale recommender systems|
|Citazione:||A., P., Gori, M., M., H., Scarselli, F., & A. C., T. (2006). Investigations into the application of Graph Neural Networks to large-scale recommender systems. SYSTEMS SCIENCE, 32(4), 17-26.|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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