Heitor Werneck

TL;DR: This paper proposes a new approach to balance exploration and exploitation in the first interactions of a new user and statistically outperforms ten benchmarks in the literature in the long run, based on the Active Learning theory.

TL;DR: This work proposes an interactive RS framework named iRec, which covers the whole experimentation process by following the main RS guidelines and contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.

TL;DR: This work proposes a new ensemble recommendation system that combines different context information (customer-product interaction, item characteristics and user behaviour) with the predictions (recommendations) of different state-of-the-art traditional Recommender Systems to recognize new patterns in user-item interaction and to ensure a desirable level of personalization for fashion domains.

TL;DR: The study's objective was to describe relations between expenditures on healthcare plans, income, and age groups, highlighting the effect of having a plan on the probability of committing more than 40% of income on health-related expenditures.