Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

SILVANA ACIAR; RAMÓN FABREGAT; TEODOR JOVÉ; GABRIELA ACIAR

Applied Sciences

Appl. Sci

Año: 2021

2076-3417

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.