Item-Based Collaborative Filtering is a recommendation technique that predicts the preference of a user for an item by analyzing the similarities between items, rather than users. This method is particularly effective in scenarios with a large number of users, as it leverages item similarity matrices to provide scalable and accurate recommendations.