Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems
Artykuł w czasopiśmie
MNiSW
140
Lista 2021
| Status: | |
| Autorzy: | Przystupa Krzysztof, Beshley Mykola, Hordiichuk-Bublivska Olena , Kyryk Maryan, Beshley Halyna, Pyrih Julia, Selech Jarosław |
| Dyscypliny: | |
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| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 8 |
| Wolumen/Tom: | 14 |
| Strony: | 1 - 24 |
| Impact Factor: | 3,252 |
| Web of Science® Times Cited: | 7 |
| Scopus® Cytowania: | 16 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This work was financed in the framework of the project Lublin University of Technology— Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).This research was supported by the project No. 0120U102201, “Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks”, and by the project No.0120U100674, “Development of the novel decentralized mobile network based on blockchain- architecture and artificial intelligence for 5G/6G development in Ukraine”. This work was financed in the framework of the project Poznan University of Technology—contract no. 0613/SBAD/4677. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 19 kwietnia 2021 |
| Abstrakty: | angielski |
| he 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. |
