ДОСЛІДЖЕННЯ ТА ЗАСТОСУВАННЯ МЕТОДІВ NLP ДЛЯ ВИРІШЕННЯ ПРОБЛЕМИ ХОЛОДНОГО СТАРТУ В РЕКОМЕНДАЦІЙНИХ СИСТЕМАХ

This work explores the application of Natural Language Processing (NLP) methods to address the cold-start problem in recommendation systems. Specifically, it investigates the use of text vectorization and clustering techniques to analyze technical articles, aiming to enhance the recommendation accuracy for new users or items with no prior interactions. Potential challenges include managing large datasets and optimizing clustering algorithms to capture the nuances of technical texts accurately. This study promises to offer valuable insights into refining recommendation systems through sophisticated text analysis methodologies.