Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems

Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.