  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.
