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A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. ELECTRONICS 2022. [DOI: 10.3390/electronics11010141] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Fast neighbor user searching for neighborhood-based collaborative filtering with hybrid user similarity measures. Soft comput 2021. [DOI: 10.1007/s00500-020-05531-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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de Aguiar Neto FS, da Costa AF, Manzato MG, Campello RJ. Pre-processing approaches for collaborative filtering based on hierarchical clustering. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Deng J, Guo J, Wang Y. A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.03.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3891-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Najafabadi MK, Mohamed AH, Mahrin MN. A survey on data mining techniques in recommender systems. Soft comput 2017. [DOI: 10.1007/s00500-017-2918-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Tsai YC, Wang SL, Kao HY, Hong TP. Edge types vs privacy in K-anonymization of shortest paths. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Nilashi M, Ibrahim OB, Ithnin N. Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowl Based Syst 2014. [DOI: 10.1016/j.knosys.2014.01.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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