Cognitive-Based Enhanced Accuracy and Relevance in Cross-Domain Recommendations

In the era of information overload, cross-domain recommendations offer a promising solution by leveraging user preferences across domains to improve recommendation accuracy and relevance. This study proposes a novel approach to cross-domain recommendations based on cognitive similarity derived from user-based features. We construct comprehensive user profiles across multiple domains by defining cognitive similarity based on user interaction data, including ratings, reviews, and genre preferences. We employ advanced feature extraction techniques, including TF-IDF for textual data and matrix factorization for latent factors, to quantify similarities in user preferences across domains. These cognitive similarity measures are then used to map user profiles into a common latent space, facilitating the generation of personalized cross-domain recommendations. To visualize the effectiveness of our approach, we use methods such as Multidimensional Scaling (MDS) and heatmaps to depict the cognitive similarity between users across different domains. Additionally, network graphs illustrate the intricate relationships and similarities across user profiles, offering intuitive insights into the recommendation process. The results demonstrate that our cognitive similarity-based approach significantly improves the relevance and diversity of cross-domain recommendations, providing a robust framework for future research and practical applications in personalized recommendation systems.