An Exploratory Study of Hierarchical Fuzzy Systems Approach in Recommendation System

TL;DR

This paper explores hierarchical fuzzy systems as an approach to improve recommendation systems by addressing the curse of dimensionality, demonstrating advantages in interpretability over traditional fuzzy logic systems.

Contribution

It introduces the use of hierarchical fuzzy systems for recommendation systems and compares their performance to fuzzy logic systems, highlighting interpretability improvements.

Findings

HFS improves interpretability in recommendation systems

HFS reduces rule complexity compared to FLS

HFS shows potential for better handling of uncertainty

Abstract

Recommendation system or also known as a recommender system is a tool to help the user in providing a suggestion of a specific dilemma. Thus, recently, the interest in developing a recommendation system in many fields has increased. Fuzzy Logic system (FLSs) is one of the approaches that can be used to model the recommendation systems as it can deal with uncertainty and imprecise information. However, one of the fundamental issues in FLS is the problem of the curse of dimensionality. That is, the number of rules in FLSs is increasing exponentially with the number of input variables. One effective way to overcome this problem is by using Hierarchical Fuzzy System (HFSs). This paper aims to explore the use of HFSs for Recommendation system. Specifically, we are interested in exploring and comparing the HFS and FLS for the Career path recommendation system (CPRS) based on four key criteria, namely topology, the number of rules, the rules structures and interpretability. The findings suggested that the HFS has advantages over FLS towards improving the interpretability models, in the context of a recommendation system example. This study contributes to providing an insight into the development of interpretable HFSs in the Recommendation systems.