Popularity Based

Popularity-based methods in recommendation systems aim to improve the accuracy and efficiency of predicting user preferences by leveraging the inherent popularity of items. Current research focuses on mitigating the negative effects of popularity bias, which can lead to overrepresentation of popular items and neglect of less-known but potentially relevant ones, through techniques like spectral regularization and popularity-aware coalescence. These advancements are significant because they improve recommendation accuracy, address the cold-start problem for new items or bundles, and offer more efficient and privacy-preserving alternatives to personalized approaches. The resulting models find applications in various domains, including online social networks and e-commerce platforms.