A proposed data analysis model to optimize the selection product process based on user personalized preferences | ||
| المجلة العلمية للبحوث والدراسات التجارية | ||
| Volume 39, Issue 3, September 2025, Pages 1539-1571 PDF (561.52 K) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/sjrbs.2025.367439.1923 | ||
| Author | ||
| samir emad* | ||
| كلية التجارة وادارة الاعمال جامعة حلوان | ||
| Abstract | ||
| A critical aim of this study is automated recommenders systems to improve their prediction accuracy and relevancy, increase the efficiency of decision-making, and improve existing problems like sparsity of data and scalability. The system’s prediction models performance is compared and evaluated against the models benchmarks using extensive datasets and stringent testing measures, including precision, recall, and f1 score. The outcome of the research shows that the model consistently delivers superior performance when compared to the other systems in accuracy of recommendations, performance, and structural changes which proves that the model is truly useful in practice. This work contributes to the growing literature in data analysis and recommendation systems by bridging the gap between user-centric design and optimization in product selection processes. The findings underline the potential of personalized models to transform industries by improving user experiences, enhancing operational efficiency, and driving sustainable growth. Ultimately, this research creates a good foundation for further studies focused on the enhancement of the integration of data analytics, machine learning, and user- centric personalization | ||
| Keywords | ||
| Data Analysis Model; Selection Product–; User personalized Preferences | ||
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