A Strategy for Expert Recommendation From Open Data Available on the Lattes Platform
This addresses the difficulty of expert recommendation for users of curriculum systems, but appears incremental as it applies existing methods to new data.
The paper tackles the problem of finding specialists from the Lattes Platform curricula by proposing a methodology for data extraction and treatment, and investigates a recommendation agent using deep neural networks with autoencoder, but no concrete results or numbers are provided.
With the increasing volume of data and users of curriculum systems, the difficulty of finding specialists is increasing.This work proposes an open data extraction methodology of the Lattes Platform curricula, a treatment for this data and investigates a Recommendation Agent approach based on deep neural networks with autoencoder.