1. Open AI. 2022. Gym documentation. https://www.gymlibrary.ml/ Open AI. 2022. Gym documentation. https://www.gymlibrary.ml/
2. CRITEO. 2022. criteo-research / reco-gym. https://github.com/criteo-research/reco-gym CRITEO. 2022. criteo-research / reco-gym. https://github.com/criteo-research/reco-gym
3. João Gomes . 2017. Boosting Recommender Systems with Deep Learning . Association for Computing Machinery (ACM) , 344–344. https://doi.org/10.1145/3109859.3109926 João Gomes. 2017. Boosting Recommender Systems with Deep Learning. Association for Computing Machinery (ACM), 344–344. https://doi.org/10.1145/3109859.3109926
4. Diogo Goncalves Farfetch Liwei Liu Farfetch João Sá Farfetch Tiago Otto Farfetch Ana Magalhães Farfetch Paula Brochado Liwei Liu João Sá Tiago Otto Ana Magalhães and Paula Brochado. 2020. The importance of brand affinity in luxury fashion recommendations. Diogo Goncalves Farfetch Liwei Liu Farfetch João Sá Farfetch Tiago Otto Farfetch Ana Magalhães Farfetch Paula Brochado Liwei Liu João Sá Tiago Otto Ana Magalhães and Paula Brochado. 2020. The importance of brand affinity in luxury fashion recommendations.
5. Diogo Goncalves , Liwei Liu , and Ana Magalhães . 2019. How big can style be? Addressing high dimensionality for recommending with style. (8 2019 ). http://arxiv.org/abs/1908.10642 Diogo Goncalves, Liwei Liu, and Ana Magalhães. 2019. How big can style be? Addressing high dimensionality for recommending with style. (8 2019). http://arxiv.org/abs/1908.10642