Co-rating Aware Evidential User-Based Collaborative Filtering Recommender System

Abstract

Recommendation systems (RS) are decision support tools created to deal with information overload, which is the main challenge of the modern digital world. The aim of RS is to provide users with interesting items based on their preferences. Collaborative filtering (CF) is the most implemented recommendation technique, it is based on the idea that similar users have similar preferences. Evidential CF is a subclass of classical CF handling uncertainty using the framework of Dempster–Shafer Theory (DST). Evidential CF recommenders (ECFRS) are suitable for critical domains such as healthcare and threat assessment, where uncertainty management remains a major challenge. In this paper, we developed a user-based evidential CF system, where the number of the co-rated items is considered in predictions generation. The proposed approach is based on Evidential KNN where the Jaccard factor is used in the neighborhood selection. Our approach is tested using Movielens dataset. Experimental results show the importance of introducing a co-rating factor in improving the recommendation quality of traditional ECFRS.