Enhancing Technology Product Recommendations: A Machine Learning Approach for GPU Classification Based on Memory Type

Accurate classification of hardware components, particularly Graphics Processing Units (GPUs), is critical for enabling intelligent recommendation systems and performance- aware configurations. This study introduces a supervised learning-based framework for categorizing GPUs by memory type a key attribute influencing computational throughput and application suitability. Utilizing a labeled dataset of GPU specifications, the proposed method evaluates the effectiveness of multiple classifiers, including Support Vector Machines and Decision Trees. Experimental results demonstrate that ensemble- based models significantly outperform others in distinguishing memory configurations, indicating their potential for integration into e-commerce recommendation engines and automated system design tools. The framework is scalable and generalizable to broader hardware classification tasks. ▢ Index Terms: GPU classification, Machine learning in hard-ware recommendations, Memory type prediction, Supervised learning for product tagging, Hardware specification analysis, Intelligent recommendation systems, Technology product categorization, SVM and Decision tree classifiers