Item Similarity

Item similarity research focuses on effectively representing and comparing items to improve recommendation systems and related tasks. Current efforts concentrate on leveraging advanced architectures like graph neural networks and large language models to capture complex relationships between items, often incorporating contrastive learning or other self-supervised techniques to address data sparsity and noise. These advancements aim to enhance the accuracy and efficiency of recommendation systems, particularly in handling large-scale datasets and diverse user behaviors, leading to improved user experience and more effective information retrieval.