Measuring Uncertainty to Extract Fuzzy Membership Functions in Recommender Systems

Nowadays, due to the high volume of choices for customers which causes confusion, the use of recommender systems is strongly growing. Of course, existing systems have two problems, one is complexity and the other is failure to consider uncertainty. In this article, we have reduced the complexity of the system by using a fuzzy innovative system and solved the problem of the uncertainty of users' ratings regarding goods. For that purpose, this research attempts to extract fuzzy membership functions from the Yahoo movie dataset for recommendation applications. In the proposed method, a type I fuzzy system with low numbers of membership functions is designed. The uncertainty in users' ratings is handled by clustering users and movies. Moreover, repeated user evaluations of the same movies are used to determine the uncertainty in improved type 1 membership functions. To evaluate the proposed strategy, MAE, confusion matrix, and Classification-report are used. The result demonstrates the superiority of the introduced strategy.