Complementary Recommendation

Complementary recommendation aims to suggest items that are useful or enjoyable *together*, rather than similar to what a user has already interacted with. Current research focuses on improving the accuracy and diversity of these recommendations, exploring methods like dual embeddings, causal inference frameworks, and meta-learning to address challenges such as data sparsity and cold-start problems for new items or businesses. These advancements are impacting e-commerce, personalized advertising, and other domains by enhancing user experience and driving sales through more effective product pairings and targeted recommendations. The field is also actively developing more holistic evaluation metrics to better assess the overall performance of complementary recommendation systems.