Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models

• Autores: Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 31, Nº 4, 2017, págs. 1031-1059
• Idioma: inglés
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• Resumen

  • Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.