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

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...

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Vydané v:	Data mining and knowledge discovery Ročník 31; číslo 4; s. 1031 - 1059
Hlavní autori:	Alexandridis, Georgios, Siolas, Georgios, Stafylopatis, Andreas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2017 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Chemistry and Earth Sciences Clustering Collaboration Computer Science Correlation Data Mining and Knowledge Discovery Factorization Filtration Graph theory Information Storage and Retrieval Logic Physics Recommendations Recommender systems Stars Statistics for Engineering Exponential random graph models Social collaborative filtering Non-negative matrix factorization Recommender systems
ISSN:	1384-5810, 1573-756X
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