Suggesting Recommendations Using Pythagorean Fuzzy Sets illustrated Using Netflix Movie Data

The web can be perceived as a huge repository of items, and users’ activities can be seen as processes of searching for items of interest. Recommender systems try to estimate what items users may like based on similarities between users, their activities, or on explicitly specified preferences. Users do not have any influence on item selection processes. In this paper we propose a novel collaborative-based recommender system that provides a user with the ability to control a process of constructing a list of suggested items. This control is accomplished via explicit requirements regarding rigorousness of identifying users who become a reference base for generating suggestions. Additionally, we propose a new way of ranking items rated by multiple users. The approach is based on Pythagorean fuzzy sets and takes into account not only assigned rates but also their number. The proposed approach is used to generate lists of recommended movies from the Netflix competition database.