Overcoming the cold-start challenge in recommender systems: A novel two-stage framework

Abstract Cold-start recommendation helps personalize user experiences and improve the relevance of the recommendation system. Despite its importance, cold-start solutions are difficult to develop because new users and items lack interaction data, making user preferences and item relevance prediction difficult. Cold-start recommendation systems face challenges because new users/items often lack historical data, resulting in suboptimal recommendation performance. This study presents the cold-start recommendation network (CSRNet) model to address the critical issue of inadequate data in new user and item recommendations, a common limitation in traditional recommendation systems. Our novel approach uses advanced machine learning techniques to create a dynamic model that adapts to no historical interaction data. Hierarchical density-based clustering groups’ subtle similarities improve recommendation accuracy when combined with transfer learning’s predictive power and Bi-GRU’s sequential data handling. Synergistic techniques optimize cold-start recommendations in this integration. By methodically overcoming data sparsity and improving recommendation quality without historical data, CSRNet sets a new standard for adaptive, accurate, and efficient recommendation systems. We tested the proposed method on a pre-processed, well-defined dataset, dividing it into training and test sets to ensure data quality and model resilience. We evaluated the CSRNet model using accuracy, precision, recall, F 1-score, Root Mean Square Error (RMSE), and Mean Absolute Error. Our model has 90.90% accuracy, 90.2% precision, 90.9% recall, 90.9% F 1-score, 1.0059 RMSE, and 0.8012 MAE, outperforming leading cold-start recommendation approaches. This shows that our model can handle the complexities of cold-start recommendation and apply it across domains to provide personalized and relevant recommendations without extensive historical interaction data.