The LinkedIn recommendation generator uses adaptive phrase technology to write a review of a business contact easier. We also ask you to fill in the minimum set of personal data that is needed to build a text. As a result, you get a ready-to-use LinkedIn recommendation, which you can edit and modify if you wish.
Our algorithms are constantly being improved as we strive to provide you with the text of the best quality, but in most cases we recommend that the final recommendation be re-checked in case of minor mistakes and inaccuracies.
The system of recommendation collection is based on the intellectual analysis of sentences. Data mining is a core of the LinkedIn recommendation samples collection system. Using our templates to write a recommendation of a LinkedIn user, you can be sure that they are not different from the actual recommendations written by a real person.
In the process of collecting and analyzing patterns of recommendation phrases, text analysts work to avoid grammatical, lexical and punctual mistakes if they have been detected in preprocessing texts. If you notice any inaccuracies that we know nothing about, please let us know.
Our database is constantly updated with new LinkedIn recommendation examples so that you can diversify your feedback. As text information expands, we improve our functionality with various tools for creating and editing review, as well as introducing many experimental functions into the LinkedIn recommendation generator.
That is why at the moment it is quite difficult to answer this question exactly, but we are trying to provide a great variety of examples so that you have something to choose from and write personalized recommendation.
