Designing a recommander system using Elasticsearch by comparing Elasticsearch, Solr and Sphinx search engines

In the increasing exchange of information between computers and mobile devices and the subject of big data, the need for software that is capable of processing and saving has increased quickly. Not just for the storage and transmission of information, it is also understandable that this is necessary for analysis of data and better service to customers and users. With the addition of users on the Internet and the growing need of people to use the Internet today it has led as the primary aim of organizations and companies such as Amazon and Google to provide better and more economic services as quickly as possible. The design of various system advisers on shopping sites and numerous social networks can show these services. It should be noted that these recommender systems increased companies' income and also employed certain people. In designing of these recommender systems, search engines say Elasticsearch has the highest power, particularly speed and security, in many respects. In addition, the design of this recommender system uses the search engine Elasticsearch and the Python programming language to implement it. In the first place, we have explained Elasticsearch and Elastic Stack, Solr, and Sphinx search engines. After that, a comparison has been made and at the end, the design of the Recommender system to be used in different systems has been discussed

Keywords:

Recommender system , Elasticsearch , Search Engines , ELK , Elastic Stack , Solr , Sphinx , Big Data , Python