Cross Domain Recommendation

Cross-domain recommendation aims to improve recommendation accuracy in data-sparse domains by leveraging information from richer domains, addressing the cold-start problem and data sparsity inherent in recommender systems. Current research focuses on developing sophisticated transfer learning techniques, often employing graph neural networks, mixture-of-experts models, and hyperbolic embeddings to effectively transfer knowledge while mitigating negative transfer and preserving user privacy. These advancements hold significant potential for enhancing the performance and robustness of recommender systems across various applications, particularly in e-commerce and online advertising, by enabling more personalized and accurate recommendations even with limited data in specific domains.