Cross Market Recommendation

Cross-market recommendation (CMR) aims to leverage data from multiple markets to improve recommendation accuracy in individual, often data-sparse, markets. Current research focuses on addressing challenges like popularity bias and data scarcity through techniques such as multi-task learning, graph neural networks, and market-aware embeddings, which efficiently model market-specific characteristics. These advancements offer more accurate and personalized recommendations across diverse markets, impacting both the efficiency of recommender systems and the user experience in global e-commerce and other multi-market applications.