Marziyeh Nourahmadi; Hojjatollah Sadeqi
Volume 3, Issue 1 , May 2022, , Pages 12-31
Abstract
Purpose: With the development of science and technology, large volumes of structured, semi-structured, and unstructured data are generated daily at breakneck speeds from various sources. This data generated by different users share many common patterns that can be filtered and analyzed to make recommendations ...
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Purpose: With the development of science and technology, large volumes of structured, semi-structured, and unstructured data are generated daily at breakneck speeds from various sources. This data generated by different users share many common patterns that can be filtered and analyzed to make recommendations for a product, goods, or service of interest to users. Recommender systems are software tools that provide suggestions to users based on their needs. One of the critical issues in recommender systems is providing personalized advice that fits the users' mood.Methodology: In this research, with a bibliometric approach using the bibliometric library in R software, all the research done on applying recommended systems in personalization is reviewed.Findings: Using the bibliometrics approach while defining recommender systems and their types, we presented an overview of the personalization field and different kinds of personalization. We also discussed the personalization process and described recommender systems as an integral part. Then, the challenges of implementing recommendation systems will be discussed, and finally, the areas in which the problem of personalization of recommendation systems can be raised.Originality/Value: The present study, with a comprehensive review of all research in this field, can help researchers imagine and select appropriate methods for classifying and analyzing data as a toolbox for using recommender systems in personalization.
