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

Rachid Ejjami

doi:10.36948/ijfmr.2024.v06i04.24598

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

Author(s)	Rachid Ejjami
Country	France
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.
Keywords	Recommendation systems, E-commerce, User experience, Data sparsity, Cold start problem, , Content-based filtering, Collaborative filtering, Matrix factorization, Hybrid approaches, Mean squared error, Root mean squared error, Precision, Recall, Hyperparameter tuning, Personalized recommendations, Customer satisfaction
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-14
Cite This	Enhancing User Experience through Recommendation Systems: A Case Study in the E-commerce Sector - Rachid Ejjami - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.24598
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.24598
Short DOI	https://doi.org/gt4ggv

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

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