Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Eirinaki, Magdalini; Gao, Jerry; Varlamis, Iraklis; Tserpes, Konstantinos

doi:10.1016/j.future.2017.09.015

Cited by 163 publications

(86 citation statements)

References 77 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

Gaol¹,

Matsuo²

2020

Preprint

View full text Add to dashboard Cite

Introduction : Social Big data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in Social Big data. If any user intends to select product such as movies, books etc. from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Case Description : Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity but not efficient for scalability. Discussion and Evaluation: The main focus of this paper is to improve scalability and provide better recommendations to users with large-scale data in less response time. We have partitioned social big graph and distributed it on different nodes based on Mahout and PowerGraph like system. Conclusion : In our approach, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using our approach. In our proposed approach ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets proves that better locality and recommendation accuracy is achieved by using our proposed approach.

show abstract

Section: Scalable Social Recommendationmentioning

confidence: 99%

The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

Gaol¹,

Matsuo²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…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%

Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

2019

Self Cite

View full text Add to dashboard Cite

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark.

show abstract

“…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%

Location recommendation privacy protection method based on location sensitivity division

Yin

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

Location-based recommendation services can provide users with convenient services, but this requires monitoring and collecting a large amount of location information. In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected. Therefore, this paper proposes a privacy protection method based on location sensitivity for location recommendation. This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels. The corresponding privacy budget is then assigned based on the sensitivity to add Laplace noise that satisfies the differential privacy. Experimental results show that this method can effectively protect the user's location privacy and reduce the impact of differential privacy noise on service quality.

show abstract

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Cited by 163 publications

References 77 publications

The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Location recommendation privacy protection method based on location sensitivity division

Contact Info

Product

Resources

About