iRec: An Interactive Recommendation Framework

Silva, Thiago; Silva, Nícollas; Werneck, Heitor; Mito, Carlos; Pereira, Adriano C. M.; Rocha, Leonardo Cristian

doi:10.1145/3477495.3531754

Cited by 9 publications

(2 citation statements)

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“…Com objetivo de responder a terceira pergunta, propomos o iRec apresentando toda a sua estrutura, bem como bases de dados, algoritmos, métricas e políticas de avaliac ¸ão de última gerac ¸ão integradas a ele, além de demonstrar como o iRec pode ser facilmente configurado para criac ¸ão de diversos experimentos no cenário de recomendac ¸ão interativa. Por fim, para responder a quarta pergunta, apresentamos a utilizac ¸ão de nosso framework, que foi instanciado em diversos cenários de recomendac ¸ão interativa, tais como: músicas, filmes, livros e pontos de interesse (POI), demonstrando toda a amplitude que um ambiente como iRec pode trazer [Silva et al 2022c].…”

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“…Publicac ¸ões: Esta dissertac ¸ão de mestrado resultou em 6 artigos: (1) SIGIR 2022 1 (A1) [Silva et al 2022c]; (2) periódico Expert Systems With Applications 2021 (A1) [Silva et al 2022a]; (3) periódico ACM Transactions on Recommender Systems [Silva et al 2023]. (4) WebMedia 2020 (A3) [Silva et al 2020]; (5) WebMedia 2021 (A3) [Silva et al 2021]; (6) WebMedia 2022 (A3) [Silva et al 2022b];…”

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iRec: Um framework para modelos interativos em Sistemas de Recomendação

Silva

Pereira²,

Rocha

2023

Anais Do XXXVI Concurso De Teses E Dissertações (CTD 2023)

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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.

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