Barbieri Distance: a metric to identify similarity between consumer profiles


  • Luiz Eugênio Barbieri Universidade de Passo Fundo (UPF)
  • Cristiano Roberto Cervi Universidade de Passo Fundo (UPF)



Content-based filtering, Levenshtein distance, Recommender systems, Similarity


This work is part of the study of recommender systems with content-based filtering, having as motivation the observation of user behavior in an Enterprise Resource Planning (ERP). The main contribution of the work is the development of Barbieri Distance, a metric whose purpose is to measure the similarity between buyers based on their purchase history. The metric is for situations where there is no buyer valuation data for the product purchased. Since it does not require ratings for items, because similarity happens when buyers buy too much or too little of the same product, it is possible to identify the similarity of the consumer profile based on their purchase history. In order to perform the metric validation experiments, a comparison method between buyer profiles is used, which presented satisfactory results in the calculation of similarity.


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How to Cite

Barbieri, L. E., & Cervi, C. R. (2020). Barbieri Distance: a metric to identify similarity between consumer profiles. ISys - Brazilian Journal of Information Systems, 13(1), 144–158.



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