Building a fuzzy logic-based McCulloch-Pitts Neuron recommendation model to uplift accuracy

Recommender system is one of the most popular technique used for information filtering. It helps in discovering hidden knowledge patterns from a large set of ubiquitous products and services. The most popular approaches such as collaborative filtering suffers from the complication of data sparsity, overspecification and high computation complexity when dataset drifts from scarcity to abundance. In this regard, we developed a hybrid model that contemplates between accuracy and computation time in order to generate a real-time most relevant items for the users. We made use of imputation technique, fuzzy logic using novel similarity technique and McCulloch-Pitts(MP) Neuron to cope up with aforementioned complications. The experimental evaluation on MovieLens dataset shows that the proposed model yields high efficiency and effectiveness. We tested the resultant classification accuracy of our proposed model using precision, recall and f1-score.