Parallelization of $Top_{k}$ Algorithm Through a New Hybrid Recommendation System for Big Data in Spark Cloud Computing Framework

Handri, Kaoutar El; Idrissi, Abdellah

doi:10.1109/jsyst.2020.3019368

Cited by 25 publications

(6 citation statements)

References 34 publications

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“…The work of the SVD method consists of finding eigenvalues and eigenvectors (denoted • and • ) [22]. Eigenvectors…”

Section: The Methods Of Singular Value Decompositionmentioning

confidence: 99%

“…Handri, K.E. et al [22] proposedhybrid recommendation systems based on Spark Cloud. In this article, the author presented a new parallel algorithm in a distributed recommendation system based on the Apache Spark platform.…”

Section: Related Workmentioning

confidence: 99%

“…The work of the SVD method consists of finding eigenvalues and eigenvectors (denoted A•A T and A T •A) [22]. Eigenvectors A T •A correspond to the columns of the matrix V. Eigenvectors A•A T correspond to the columns of the matrix U.…”

Section: ) Application Of the Singular Value Decomposition In Recomme...mentioning

confidence: 99%

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Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

et al. 2021

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The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

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“…The work of the SVD method consists of finding eigenvalues and eigenvectors (denoted • and • ) [22]. Eigenvectors…”

Section: The Methods Of Singular Value Decompositionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

et al. 2021

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“…Dahdouh et al [4] adopted the Hadoop platform to manage and recommend resources and completed massive resource management and push with the help of Hadoop. El Handri and Idrissi [5] used the Spark platform to recommend a large number of resources to improve the efficiency of resource recommendation. Both are recommendation studies based on massive resources, focusing more on the construction of a cloud computing data push platform, without in-depth development of microresource details and methods.…”

Section: Introductionmentioning

confidence: 99%

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

Wang

2022

Advances in Multimedia

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With the accelerated trend of globalization and modernization, many provincial intangible cultural heritages (PICH) are in danger of being lost. In the context of Internet technology, the use of digital multimedia for personalized recommendation is an effective way to promote the transmission of intangible cultural heritage. However, traditional recommendation systems tend to treat different members as homogeneous objects, ignoring the relationship between members’ professional backgrounds and the inherent properties of items, and cannot truly solve the problem of conflicting preferences in the integration process. In view of this, this paper proposes a group recommendation system based on nonnegative matrix decomposition. First, the group rating information is decomposed into a user matrix and item matrix by nonnegative matrix decomposition. Then, the item affiliation matrix and member expertise matrix are calculated by using the affiliation and expertise weights for the two matrices, respectively, and the contribution degree of each member to different item ratings is obtained from them. Finally, the group preference model is constructed by weighted fusion of members’ preferences based on their contribution degrees, and different recommendation lists are generated for different user preferences. The experimental results prove that this system has high recommendation accuracy in cross-regional promotion of PICH.

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“…Such problems may be overcome with the use of the MCDA method. Indeed, MCDA has been used in recommender systems for refining the suggestion of content to users [26][27][28], but to the best of our knowledge, it has not been applied yet sufficiently in digital repositories. In a review work of 2019 [29] about recommender systems for digital repositories, the author confirms that the main algorithms used by such systems are mainly the above ones, including content-based and collaborative filtering and hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

Troussas

Krouska

Sgouropoulou

2021

Advances in Human-Computer Interaction

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Digital repositories contain a large amount of content, which is available to heterogeneous groups of people. As such, in many cases people encounter difficulties in finding specific content which is related to their preferences. In view of this compelling need and towards advancing human-computer interaction, this paper presents a recommender system which is incorporated in a digital repository. The recommender system is designed using multiple-criteria decision analysis (MCDA) and more specifically the weighted sum model (WSM) in order to refine the delivered content to the users. It also considers several users’ characteristics (their preferences as depicted by the content they visited or searched and by the frequency of searches/visits) and features of the content (content types and traffic). The recommender system outputs the suggestions of content to users based on their preferences and interests. The presented recommender system was evaluated by real users, and the results show a high degree of accuracy in the recommended content and satisfaction by users.

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Parallelization of $Top_{k}$ Algorithm Through a New Hybrid Recommendation System for Big Data in Spark Cloud Computing Framework

Cited by 25 publications

References 34 publications

Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

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