Item Representation

Item representation in recommender systems focuses on creating effective numerical representations of items to accurately capture user preferences and facilitate personalized recommendations. Current research emphasizes incorporating multimodal data (text, images, user reviews), leveraging graph neural networks to model item relationships, and employing advanced architectures like transformers and contrastive learning to improve representation quality and address challenges like cold-start items and data sparsity. These advancements lead to more accurate and robust recommendation systems with broader applicability across various domains, impacting both the efficiency of recommendation algorithms and the user experience.