Improving Recommender System Accuracy with Category-Specific Techniques

Abstract

Recommender systems play a crucial role in guiding users towards items they are likely to appreciate (13). Traditional collaborative filtering (CF) methods have been widely used (1), but they often face challenges such as data sparsity and cold start problems (10). This research explores the integration of category-specific algorithms with traditional CF to enhance the performance of recommender systems. By considering item categories, we aim to create hybrid models that leverage the strengths of both approaches, resulting in more accurate and personalized recommendations. Our study is grounded in the extensive evaluation of different models, including user-based CF (2), category-based CF (CCF), and a hybrid model combining both approaches (3). We utilized the MovieLens 1M dataset, which contains over a million ratings from thousands of users, to validate our models. The performance of these models was assessed using precision, recall, and F1-score metrics, which are standard measures in recommender system research. The results indicate that incorporating category-specific information significantly improves the performance of recommender systems. The CCF model outperformed the traditional CF model, demonstrating the value of considering item categories. Furthermore, the hybrid model, which combines CF and CCF, achieved the highest performance, illustrating the effectiveness of leveraging the strengths of both methods. This research contributes to the field of recommender systems by providing a novel approach that enhances recommendation accuracy and personalization. The findings suggest that integrating categoryspecific algorithms with traditional CF methods can lead to significant improvements in recommendation performance, offering valuable insights for future research and practical applications in various domains such as e-commerce, streaming services, and social media platforms.