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As the volume of data on the web continue to increase, it is getting more challenging for the search mechanism to find with a high precision rate what the users want to find. As a solution to improve these results, the development of a recommender engine, based on the content of the documents, would prove itself very useful. In this context, this research has the objective to show how the current search and indexing tools could be improved with recommendation, Machine Learning and textual analysis algorithms. The idea behind these project would be to, based on the content of the documents recovered in the search, find similar documents using most of the Open Source technology we have available right now. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SBSI 2015, May 26th-29th, 2015, Goiânia, Goiás, Brazil Copyright SBC 2015.

Uso de Ferramentas Livres para Recuperação de Conteúdos Textuais em Ambientes Big Data