A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/1/141 |
| _version_ | 1797499253866627072 |
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| author | Hyeyoung Ko Suyeon Lee Yoonseo Park Anna Choi |
| author_facet | Hyeyoung Ko Suyeon Lee Yoonseo Park Anna Choi |
| author_sort | Hyeyoung Ko |
| collection | DOAJ |
| description | This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied. |
| first_indexed | 2024-03-10T03:44:50Z |
| format | Article |
| id | doaj.art-c9216bd3bb154f11a6e242aeaf0fb398 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T03:44:50Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c9216bd3bb154f11a6e242aeaf0fb3982023-11-23T11:23:32ZengMDPI AGElectronics2079-92922022-01-0111114110.3390/electronics11010141A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application FieldsHyeyoung Ko0Suyeon Lee1Yoonseo Park2Anna Choi3Department of Digital Media Design and Applications, Seoul Women’s University, Seoul 01797, KoreaDepartment of Computer Science & Engineering, Seoul Women’s University, Seoul 01797, KoreaDepartment of Digital Media Design and Applications, Seoul Women’s University, Seoul 01797, KoreaDepartment of Computer Science & Engineering, Seoul Women’s University, Seoul 01797, KoreaThis paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.https://www.mdpi.com/2079-9292/11/1/141recommender systemrecommendation systemcontent-based filteringcollaborative filteringhybrid systemrecommendation algorithm |
| spellingShingle | Hyeyoung Ko Suyeon Lee Yoonseo Park Anna Choi A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields Electronics recommender system recommendation system content-based filtering collaborative filtering hybrid system recommendation algorithm |
| title | A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields |
| title_full | A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields |
| title_fullStr | A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields |
| title_full_unstemmed | A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields |
| title_short | A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields |
| title_sort | survey of recommendation systems recommendation models techniques and application fields |
| topic | recommender system recommendation system content-based filtering collaborative filtering hybrid system recommendation algorithm |
| url | https://www.mdpi.com/2079-9292/11/1/141 |
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