A post-processing strategy in a papers recommendation system for improving the diversity

• Ediana da Silva de Souza UDESC
• Daniel Lichtnow UFSM
• Isabela Gasparini UDESC

DOI: https://doi.org/10.5753/brasnam.2022.222805

Abstract

Recommender Systems seek to identify items that are relevant to their users. One of the recent topics of interest in the Recommender Systems area is the diversity issues. When considering aspects to increase diversity, we try to avoid prioritizing some of the user's preferences or recommending items that are very similar to each other. This work presents the implementation of a postprocessing model in a Papers Recommendation System, aiming to promote the diversity of recommended papers. The system was used by 56 users from different institutions, with diversity and precision being evaluated by metrics. The post-processing model increased the diversity of recommendations while maintaining the precision.

Keywords: recommender systems, diversity, user profile, post-processing

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