Enhancing User Experience through Recommendation Systems: A Case Study in the E-commerce Sector

Abstract

The significance of recommendation system techniques in improving user experience on e-commerce platforms should be considered as data-driven technologies progress. Conventional recommendation systems frequently need help dealing with issues such as data sparsity, cold start problems, and scalability, which significantly affect the ability of e-commerce platforms to offer accurate and relevant recommendations to consumers. This study evaluates the effectiveness of different recommendation system methodologies, including Averaging, Content-Based Filtering, Collaborative Filtering, Matrix Factorization, and Hybrid Approaches. Utilizing an extensive dataset from an e-commerce company, which encompasses consumer and product information such as ProductID, ProductName, Category, Price, CustomerID, and RatingReview, the dataset is divided into training and testing sets to assess the accuracy of the recommendation system models. Metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, Precision, and Recall are used for this evaluation. Preliminary results indicate that applying advanced recommendation system techniques, especially after optimizing hyperparameters, dramatically improves the accuracy of recommendations and increases user satisfaction compared to more straightforward methods. These findings can revolutionize recommendation tactics on e-commerce platforms, leading to more tailored and gratifying user experiences. The outcomes of this research are expected to enhance the optimization of recommendation systems and broaden the current knowledge on this topic within e-commerce.