An Intelligent Weighted Recommendation Technique utilizing Ensemble System for Enhanced Prediction Accuracy for better Consumer Decision
Keywords:
Intelligent Recommendation System, Machine Learning, Multi-nomial Naïve Bayes, Multi-Layer Perceptron, Logistic Regression, and Ensemble ClassifiersAbstract
A recommendation system can intelligently employ machine learning algorithms to suggest diverse options tailored to user interests based on multiple sources of information. Most recommendation systems heavily rely on the collaborative filtering (CF) approach, where user preference data is amalgamated with that of other users to predict additional items of potential interest to the consumer. In this study, an innovative weighted recommendation system is developed to enhance consumer decision-making using CF. Equations to calculate the weight of both the product and review, as well as the similarity between consumer reviews, are devised in the methodology. The methodology employs machine learning techniques such as Multi-nomial Naïve Bayes (MNB), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) as intelligent ensemble models. Ensemble Classifiers (MNB+MLP+LR) are utilized to implement the methodology's results, aiming for superior outcomes compared to previous studies. The proposed model is trained and tested using an open-source dataset. Numerical analysis of the proposed model demonstrates its superior performance over conventional methods in terms of accuracy (0.952), precision (0.908), recall (0.897), F-measure (0.941), error rate (0.087), and other metrics.
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https://www.kaggle.com/code/haojie98/amazon-product-recommendations/data
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