# How Amazon Created a Marketplace for Recommendation Algorithms > A system that lets website owners rent recommendation algorithms from third-party developers, with a built-in payment structure that rewards developers based on how well their algorithms perform. - **Patent:** US 8249948 - **Original title:** System for obtaining recommendations from multiple recommenders - **Owner:** Amazon Technologies Inc - **Granted:** 2012 - **Status:** Active - **Times cited:** 3 - **Field:** ecommerce, software, ai_ml ## What it does This patent describes a centralized platform where third-party developers can upload recommendation algorithms, which website operators can then integrate into their own sites to suggest products to users. The system acts as a middleman, tracking how often each algorithm is used and how successful it is at driving user actions, such as clicks or purchases. Crucially, the system automates the financial side: it charges the website operator for the recommendations and distributes a portion of that revenue back to the algorithm developer. The payout to the developer is directly tied to the performance metrics of their specific algorithm, creating a performance-based incentive model. ## What it does NOT cover - Does not cover recommendation algorithms that operate in isolation without a centralized marketplace or clearinghouse for payments. - Does not cover systems where developers are paid a flat fee regardless of the algorithm's performance or user engagement metrics. - Does not cover the specific mathematical logic or code inside the recommendation algorithms themselves. ## The clever bit The innovation is the integration of performance-based compensation directly into the software distribution model, effectively gamifying the creation of recommendation algorithms by tying developer income to real-time user conversion data. ## Real-world examples 1. Amazon Personalize 2. Google Cloud Recommendations AI 3. Third-party recommendation plugins for Shopify stores ## Why it matters This patent formalized the 'app store' model for backend software services. By creating a financial incentive for developers to build better recommendation engines, it helped shift the industry away from custom-built, static recommendation tools toward dynamic, competitive marketplaces where the best-performing algorithms win. ## Frequently asked questions ### What does How Amazon Created a Marketplace for Recommendation Algorithms cover? A system that lets website owners rent recommendation algorithms from third-party developers, with a built-in payment structure that rewards developers based on how well their algorithms perform. ### Who owns patent US 8249948? Amazon Technologies Inc owns this patent, granted in 2012. ### When does this patent expire? This patent is expected to expire on August 21, 2032, when the invention enters the public domain. ### What is patent US 8249948 cited by? This patent has been cited by 3 later patents that build on its ideas. ### What problem does this patent solve? This patent formalized the 'app store' model for backend software services. By creating a financial incentive for developers to build better recommendation engines, it helped shift the industry away from custom-built, static recommendation tools toward dynamic, competitive marketplaces where the best-performing algorithms win. ### What does this patent NOT cover? Does not cover recommendation algorithms that operate in isolation without a centralized marketplace or clearinghouse for payments. **Full plain-English explainer:** https://patentbrief.org/patent/us/8249948/facebook-like-button **Original patent:** https://patents.google.com/patent/US8249948 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How a System Finds and Creates Machine Learning Models](https://patentbrief.org/patent/us/12340293/machine-learning-model-repository-management-and-search-engine) — This patent describes a system that helps users find existing machine learning models or algorithms for a specific task and, if needed, automatically trains a new model using a selected algorithm. - [How Eventbrite Recommends Events Based on Your Social Network](https://patentbrief.org/patent/us/8700540/facebook-messenger) — A system that suggests events to you by analyzing your social media connections and your past attendance history to see what your friends are doing. - [How a Recommendation System Uses Dislikes to Suggest New Content](https://patentbrief.org/patent/us/11416536/content-recommendation-system-11416536) — Comcast's patent describes a content recommendation system that finds users with similar tastes by focusing on what content they both dislike, especially popular disliked items, to suggest new shows or movies. - [How a Recommendation System Learns What You Really Want to Watch](https://patentbrief.org/patent/us/12118030/dynamic-feedback-in-a-recommendation-system) — This patent describes a media recommendation system that creates a unique user profile, uses it to pick a starting point, and then dynamically adjusts a list of suggestions based on what you've already watched and your real-time feedback. - [How a System Ranks Content Based on Friend Recommendations](https://patentbrief.org/patent/us/20230245158/content-recommendation-system) — This patent describes a system that recommends content, like TV shows, by tracking how many friends accept social media recommendations and then ranking content based on how fast those acceptances are growing.