The role of feature selection in personalized recommender systems
Roger Bagué-Masanés, Verónica Bolón‐Canedo, Beatriz Remeseiro · 2022
Recommender systems suggest products to users, based on their popularity or the users' preferences.This paper proposes a hybrid personalized recommender system based on users' tastes and also on information available about items.We used a dataset downloaded from Tri-pAdvisor, which contains some information from restaurants (items), such as price range or special diets.Feature selection techniques are employed to analyze the impact that each variable has on personalized recommendations, allowing us to understand not only the process underlying the recommendation to favor the transparency of the system, but also what users value the most when choosing a restaurant.