Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper provides a comprehensive analysis of the evolution and current landscape of recommendation systems, extensively used across various web applications. It categorizes recommendation techniques into four main types: content-based, collaborative filtering, knowledge-based, and hybrid a...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10798416/ |
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| author | Ziyuan Xia Anchen Sun Jingyi Xu Yuanzhe Peng Rui Ma Minghui Cheng |
| author_facet | Ziyuan Xia Anchen Sun Jingyi Xu Yuanzhe Peng Rui Ma Minghui Cheng |
| author_sort | Ziyuan Xia |
| collection | DOAJ |
| description | This survey paper provides a comprehensive analysis of the evolution and current landscape of recommendation systems, extensively used across various web applications. It categorizes recommendation techniques into four main types: content-based, collaborative filtering, knowledge-based, and hybrid approaches, tailored for specific user contexts. The review spans historical developments to cutting-edge innovations, with a focus on big data analytics applications, state-of-the-art recommendation models, and evaluation using prominent datasets like MovieLens, Amazon Reviews, Netflix Prize, Last.fm, and Yelp. The paper addresses significant challenges such as data sparsity, scalability, and the need for diverse recommendations, highlighting these as key directions for future research. It also explores practical applications and the integration challenges of recommendation systems in everyday life, underscoring the potential of big data-driven advancements to significantly enhance real-world experiences. |
| format | Article |
| id | doaj-art-3fdac7b520004e70ae4e391a2f8f3268 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3fdac7b520004e70ae4e391a2f8f32682025-08-20T02:09:01ZengIEEEIEEE Access2169-35362024-01-011219691419692810.1109/ACCESS.2024.351749210798416Contemporary Recommendation Systems on Big Data and Their Applications: A SurveyZiyuan Xia0https://orcid.org/0009-0005-9873-0603Anchen Sun1https://orcid.org/0000-0003-3633-6385Jingyi Xu2Yuanzhe Peng3https://orcid.org/0000-0002-4900-6118Rui Ma4https://orcid.org/0000-0003-0962-5421Minghui Cheng5https://orcid.org/0000-0002-8983-5148Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USADepartment of Architecture, Cornell University, Ithaca, NY, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USABascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL, USADepartment of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USAThis survey paper provides a comprehensive analysis of the evolution and current landscape of recommendation systems, extensively used across various web applications. It categorizes recommendation techniques into four main types: content-based, collaborative filtering, knowledge-based, and hybrid approaches, tailored for specific user contexts. The review spans historical developments to cutting-edge innovations, with a focus on big data analytics applications, state-of-the-art recommendation models, and evaluation using prominent datasets like MovieLens, Amazon Reviews, Netflix Prize, Last.fm, and Yelp. The paper addresses significant challenges such as data sparsity, scalability, and the need for diverse recommendations, highlighting these as key directions for future research. It also explores practical applications and the integration challenges of recommendation systems in everyday life, underscoring the potential of big data-driven advancements to significantly enhance real-world experiences.https://ieeexplore.ieee.org/document/10798416/Recommendation systembig datamachine learningsustainability |
| spellingShingle | Ziyuan Xia Anchen Sun Jingyi Xu Yuanzhe Peng Rui Ma Minghui Cheng Contemporary Recommendation Systems on Big Data and Their Applications: A Survey IEEE Access Recommendation system big data machine learning sustainability |
| title | Contemporary Recommendation Systems on Big Data and Their Applications: A Survey |
| title_full | Contemporary Recommendation Systems on Big Data and Their Applications: A Survey |
| title_fullStr | Contemporary Recommendation Systems on Big Data and Their Applications: A Survey |
| title_full_unstemmed | Contemporary Recommendation Systems on Big Data and Their Applications: A Survey |
| title_short | Contemporary Recommendation Systems on Big Data and Their Applications: A Survey |
| title_sort | contemporary recommendation systems on big data and their applications a survey |
| topic | Recommendation system big data machine learning sustainability |
| url | https://ieeexplore.ieee.org/document/10798416/ |
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