Please use this identifier to cite or link to this item:
http://univ-bejaia.dz/dspace/123456789/24803
Title: | A hybrid recommender system for enhanced e-commerce recommendations. |
Authors: | Saci, Souha Merzouk, Salas Akilal, Karim;promoteur |
Keywords: | Recommendation system : Machine Learning : SVD algorithm :TF-IDF |
Issue Date: | 4-Jul-2024 |
Publisher: | Université Abderramane Mira-Bejaia |
Abstract: | The e-commerce sector has experienced significant growth, leading industry players to implement recommendation systems aimed at enhancing user satisfaction and boosting revenue through personalized suggestions. As a result, hybrid filtering techniques combining collaborative filtering (CF) and content-based methods have emerged as a promising approach to capture user preferences effectively Our study presents a novel hybrid recommendation system for e-commerce that combines content-based filtering using inverse document frequency-term frequency (IDF-TF) with collaborative filtering through singular value decomposition (SVD). By integrating linear regression and leveraging the ? parameter, we effectively balance between contentbased and collaborative filtering to address cold start issues and data sparsity. This approach enhances recommendation accuracy and quality, as demonstrated by our experiments on real e-commerce data, focusing on scalability, accuracy, and coverage. |
Description: | Option :Systéme d’Information Avancés |
URI: | http://univ-bejaia.dz/dspace/123456789/24803 |
Appears in Collections: | Mémoires de Master |
Files in This Item:
File | Description | Size | Format | |
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Memoire final.pdf | 2.15 MB | Adobe PDF | View/Open |
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