An automatic product recommendation system in e-commerce using Flamingo Search Optimizer and Fuzzy Temporal Multi Neural Classifier

In this paper, a new automatic product recommendation system (APRS) is proposed to recommend the suitable products to the customer in e-commerce by analyzing the customers’ reviews. This recommendation system applies semantic aware data preprocessing, feature selection and extraction and classification. The initial level data preprocessing including blank space and stop word removal. Moreover, we use a Flamingo Search Optimizer (FSO) for optimizing the features that are extracted in the initial level data preprocessing. In addition, a new Fuzzy Temporal Multi Neural Classification Algorithm (FTMNCA) is proposed for performing effective classification that is helpful to make effective decision on prediction process. In addition, the proposed automatic product recommendation system recommends the suitable products to the customers according to the classification result. Finally, the proposed system is evaluated by conducting various experiments and proved as superior than the available systems in terms of prediction accuracy, precision, recall and f-measure.