Typology of personalization in recommender systems

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, goods3, or service of interest to users. Recommender systems are software tools that are used to 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.

In this research, with the bibliometrics approach using bibliometrix library in R software, all the researches done on the application of recommended systems in personalization is reviewed.

In this research, using the bibliometrics approach, while defining recommender systems and their types, an overview of the field of personalization is introduced, and different types of personalization are presented. It also discusses the process of personalization and describes recommender systems as an integral part of this process. The following are the challenges that exist for implementing recommendation systems, and finally, the areas in which the issue of personalization of recommendation systems can be raised.

The results of this study with a comprehensive review of all research in this field can help researchers in ideation and selection of appropriate methods in classifying and analyzing data as a toolbox for the use of recommender systems in personalization.