iRec: Um framework para modelos interativos em Sistemas de Recomendação - Silva, Thiago; Pereira, Adriano; Rocha, Leonardo (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2023: Anais do XXXVI Concurso de Teses e DissertaçõesDO - 10.5753/ctd.2023.229296, 2023)
Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned with the accuracy of each method. Thus, this master dissertation proposes an interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.