A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems

  • Yan Andrade UFSJ
  • Nícollas Silva UFMG
  • Thiago Silva UFSJ
  • Adriano Pereira UFMG
  • Diego Dias UFSJ
  • Elisa T. Albergaria UFSJ
  • Leonardo Rocha UFSJ

Resumo


Interactive recommendation has been recognized as a Multi-Armed Bandit (MAB) problem. Items are arms to be pulled (i.e., recommended) and the user’s satisfaction is the reward to be maximized. Despite the advances, there is still a lack of consensus on the best practices to evaluate such solutions. Recently, two complementary frameworks were proposed to evaluate bandit solutions more accurately: iRec and OBP. The first one has a complete set of offline metrics and bandit models that allows us to perform an comparisons with several evaluation policies. The second one provides a huge set of bandit models to be evaluated through several counterfactual estimators. However, there is a room to be explored when joining these two frameworks. We propose and evaluate an integration between both, demonstrating the potential and richness of such combination.
Palavras-chave: Contextual Bandits, Offline Evaluation, Counterfactual Evaluation

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Publicado
23/10/2023
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ANDRADE, Yan; SILVA, Nícollas; SILVA, Thiago; PEREIRA, Adriano; DIAS, Diego; ALBERGARIA, Elisa T.; ROCHA, Leonardo. A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 193–197.

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