You just finished watching Narcos, season 2 and are about to slip into the post-binge-watching doldrums, when up pops a recommendation from Netflix. Applications are getting smarter every day and they rely on developers and technologies to pull out insights for recommendations. Effective recommendations often rely on similarities between users, such as geography, demographic, past preferences, and past dislikes. These similarities can be used to generate recommendations by comparing users who share the same characteristics with those who have done things in the same order or liked items that you haven't looked at yet. Redis is a database that supports sophisticated set operations, which can be used to implement recommendation algorithms. By using simple set intersection and union operations, similar users can be found based on their ratings of common items, resulting in similarity scores that can be used to generate top recommendations for users. The use of Redis Sorted Sets allows for blazing fast retrieval times, making it possible to implement a high-performance recommendation engine with minimal effort.