Investigation into the application of graph neural networks to large-scale recommender systems

• Autorzy: Pucci A. , Gori M. , Hagenbuchner M. , Scarselli F. , Tsoi A. C.
• Języki publikacji: EN

Abstrakty

EN

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 o 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.

Słowa kluczowe

EN

graph neural networks; recommender systems

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2006
• Tom: Vol. 32, no 4
• Strony: 17--26
• Opis fizyczny: Bibliogr. 18 poz., wykr.

Twórcy

autor

Pucci A.

autor

Gori M.

autor

Hagenbuchner M.

autor

Scarselli F.

autor

Tsoi A. C.

• Dipartimento di Ingegneria dell'Informazione, University of Siena, Via Roma, 56, Siena, Italy,

Bibliografia

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