Nowadays, Recommender Systems have played a crucial role in several entertainment scenarios by making personalised recommendations and guiding the entire users’ journey from their first interaction. Recent works have addressed it as a Contextual Bandit by providing a sequential decision model to explore items not tried yet (or not tried enough) or exploit the best options learned so far. However, this work noticed these current algorithms are limited to naive non-personalised approaches in the first interactions of a new user, offering random or most popular items. Through experiments in three domains, we identify a negative impact of these first choices. Our study indicates that the bandit performance is directly related to the choices made in the first trials. Then, we propose a new approach to balance exploration and exploitation in the first interactions and handle these drawbacks. This approach is based on the Active Learning theory to catch more information about the new users and improve their long-term experience. Our idea is to explore the potential information gain of items that can also please the user’s taste. This method is named Warm-Starting Contextual Bandits, and it statistically outperforms 10 benchmarks in the literature in the long run.

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 about the accuracy of each method. Thus, this work 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.

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering different aspects besides accuracy. Due to the lack of reproducible and extensible benchmarks, our work introduces a reproducibility framework for POI methods based on a collection of Python software libraries and a Docker image. Our proposal is composed of: (1) a package to perform a protocol that reproduces our systematic mapping process Werneck et al. (2021), containing all collected data, insightful views on current advances and opened challenges; and (2) an extensible benchmark to perform a protocol to reproduce experimental evaluations on POI recommendation, considering different datasets, metrics, and the strongest baselines in the literature. This work also demonstrates all processes required to instantiate its framework. Moreover, our work can be considered at least weakly reproducible, since we were able to reproduce the results of the previous paper, leading us to the same conclusions.