Item Based Collaborative Filtering

Item-based collaborative filtering recommends items similar to those a user has previously interacted with, aiming to improve the accuracy and efficiency of recommendation systems. Current research focuses on enhancing these systems through advanced techniques like graph neural networks, which leverage item relationships to improve recommendation accuracy, and the incorporation of learned similarity functions for more efficient retrieval. These advancements address challenges such as data sparsity, cold-start problems, and the need for efficient online deployment, ultimately leading to more personalized and effective recommendations in various applications.