Time Cluster Personalized Ranking Recommender System in Multi-Cloud
Recommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1300 |
| _version_ | 1797610346829053952 |
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| author | S. Abinaya K. Indira S. Karthiga T. Rajasenbagam |
| author_facet | S. Abinaya K. Indira S. Karthiga T. Rajasenbagam |
| author_sort | S. Abinaya |
| collection | DOAJ |
| description | Recommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they make predictions within small clusters. The major challenges of recommender systems are accuracy and scalability. Traditionally, recommendation systems are based on a centralized framework that restrains quick scalability for enormous data volumes. The emergence of cloud technology resolves this issue as it handles vast data and supports massive processing. This paper proposes a time cluster personalized ranking recommender system (TCPRRS) in a multi-cloud environment. TCPRRS is a five-stage system that generates recommendations based on temporal information of user consumption and clustering with personalized ranking. Particle swarm optimization (PSO) is utilized for optimizing the solution. The efficiency of TCPRRS is estimated using similarity metrics. |
| first_indexed | 2024-03-11T06:13:11Z |
| format | Article |
| id | doaj.art-3efbe934483c4b0299a999243e08f020 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:11Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3efbe934483c4b0299a999243e08f0202023-11-17T12:26:41ZengMDPI AGMathematics2227-73902023-03-01116130010.3390/math11061300Time Cluster Personalized Ranking Recommender System in Multi-CloudS. Abinaya0K. Indira1S. Karthiga2T. Rajasenbagam3School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaDepartment of Information Technology, Thiagarajar College of Engineering, Madurai 625015, IndiaDepartment of Information Technology, Thiagarajar College of Engineering, Madurai 625015, IndiaDepartment of Computer Science and Engineering, Government College of Technology, Coimbatore 641013, IndiaRecommender systems have become a vital tool to identify items for users based on personalized preferences. The personalized ranking or item recommendation generates a ranked list of items for the users. Clustering methods offer better scalability than collaborative filtering (CF) methods since they make predictions within small clusters. The major challenges of recommender systems are accuracy and scalability. Traditionally, recommendation systems are based on a centralized framework that restrains quick scalability for enormous data volumes. The emergence of cloud technology resolves this issue as it handles vast data and supports massive processing. This paper proposes a time cluster personalized ranking recommender system (TCPRRS) in a multi-cloud environment. TCPRRS is a five-stage system that generates recommendations based on temporal information of user consumption and clustering with personalized ranking. Particle swarm optimization (PSO) is utilized for optimizing the solution. The efficiency of TCPRRS is estimated using similarity metrics.https://www.mdpi.com/2227-7390/11/6/1300clusteringpersonalized rankingparticle swam optimizationrecommender systemcollaborative filteringuser interest |
| spellingShingle | S. Abinaya K. Indira S. Karthiga T. Rajasenbagam Time Cluster Personalized Ranking Recommender System in Multi-Cloud Mathematics clustering personalized ranking particle swam optimization recommender system collaborative filtering user interest |
| title | Time Cluster Personalized Ranking Recommender System in Multi-Cloud |
| title_full | Time Cluster Personalized Ranking Recommender System in Multi-Cloud |
| title_fullStr | Time Cluster Personalized Ranking Recommender System in Multi-Cloud |
| title_full_unstemmed | Time Cluster Personalized Ranking Recommender System in Multi-Cloud |
| title_short | Time Cluster Personalized Ranking Recommender System in Multi-Cloud |
| title_sort | time cluster personalized ranking recommender system in multi cloud |
| topic | clustering personalized ranking particle swam optimization recommender system collaborative filtering user interest |
| url | https://www.mdpi.com/2227-7390/11/6/1300 |
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