2018
DOI: 10.1016/j.future.2017.09.015
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Abstract: Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend… Show more

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Cited by 150 publications
(66 citation statements)
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References 77 publications
(74 reference statements)
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“…In [2], it is mentioned that recommendation process can be enhanced by adapting to dynamically changing graph and process large-scale graphs. Data variety, volatility and volume are the major issues which need to be addressed by scalable recommender systems.…”
Section: Scalable Social Recommendationmentioning
confidence: 99%
“…In [2], it is mentioned that recommendation process can be enhanced by adapting to dynamically changing graph and process large-scale graphs. Data variety, volatility and volume are the major issues which need to be addressed by scalable recommender systems.…”
Section: Scalable Social Recommendationmentioning
confidence: 99%
“…Collaborative Filtering algorithms have been the state-of-the-art solution for the generation of recommendations in various social network applications. Two of the main problems that CF algorithms have to confront [21] are the information sparsity and the lack of scalability in huge datasets. Recent advances in CF algorithms capitalize on the use of deep neural network architectures for adding implicit feedback [22] to the original rating matrix, thus creating a latent space with reduced sparsity.…”
Section: Related Workmentioning
confidence: 99%
“…The tendency of using computer clusters to combine processing power and distribute the processing load has affected the implementation of various CF algorithms to deal with the scalability issues over the last years. Based on this, Java Threads, Pthreads, OpenMP frameworks for parallel programming and Mahout, Apache Spark for data processing have been utilized to implement collaborative filtering [21,28].…”
Section: Related Workmentioning
confidence: 99%
“…This makes it possible to provide users with ubiquitous mobile network services. In particular, the rise of mobile social networks has greatly helped users in network information services [1]. At the same time, since it is necessary to collect a large amount of information from users while enjoying network services, how to ensure the data security of supervisory control and data acquisition (SCADA) system is worthy of attention.…”
Section: Introductionmentioning
confidence: 99%