# How a Recommendation System Learns What You Really Want to Watch > 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. - **Patent:** US 12118030 - **Original title:** Dynamic feedback in a recommendation system - **Owner:** Malibu Entertainment - **Granted:** 2024 - **Status:** Active - **Times cited:** 0 - **Field:** consumer_electronics, software, telecommunications, ai_ml, gaming ## What it does The system first builds a "profile vector" for a user (Claim 1). This profile is clever because it gives less weight to media items the user has watched many times, encouraging new discoveries. For example, if you've watched "The Matrix" 50 times, that movie's influence on your profile might be reduced. Next, it picks a "seed media item" based on this profile, which acts as a starting point for recommendations (Claim 1). This seed could be a trending show or something you recently liked (Claim 4). Then, the system creates a "pool" of other media items and sorts them by how similar they are to the seed item (Claim 1). Finally, it selects an item from this sorted pool for you to watch (Claim 1). Crucially, if you give feedback, like skipping a song or rating a movie, the system instantly updates the pool with new suggestions (Claim 3). ## What it does NOT cover - Does not cover recommendation systems that do not reduce the influence of frequently consumed media items in the user profile. - Does not cover systems that generate recommendations without first establishing a "seed media item" based on the user's profile. - Does not cover systems where the pool of media items is not sorted by vector distance to a seed item. - Does not cover systems that do not dynamically modify the recommendation pool based on user feedback. - Does not cover systems that rely solely on explicit user ratings without considering consumption frequency. ## The clever bit The truly novel aspect is how the user's profile is generated: it's "inversely proportional to a total number of times that the user has consumed a specific media item" (Claim 1). This means the system actively reduces the weight of content you've watched repeatedly, pushing it to recommend fresh, relevant items rather than endlessly suggesting your all-time favorites. ## Real-world examples 1. Netflix's "Because you watched..." suggestions 2. Spotify's Discover Weekly playlist 3. YouTube's video recommendations 4. TikTok's For You Page algorithm 5. Amazon's "Customers who bought this also bought..." ## Why it matters Recommendation systems are fundamental to how users discover content across streaming platforms, social media, and e-commerce. This patent aims to refine how these systems learn user preferences, potentially leading to more engaging and less repetitive content discovery experiences. By dynamically adapting to user feedback and de-emphasizing over-consumed content, it seeks to improve user satisfaction and retention in competitive media markets. ## Frequently asked questions ### What does How a Recommendation System Learns What You Really Want to Watch cover? 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. ### Who owns patent US 12118030? Malibu Entertainment owns this patent, granted in 2024. ### When does this patent expire? This patent is expected to expire on July 19, 2043, when the invention enters the public domain. ### What problem does this patent solve? Recommendation systems are fundamental to how users discover content across streaming platforms, social media, and e-commerce. This patent aims to refine how these systems learn user preferences, potentially leading to more engaging and less repetitive content discovery experiences. By dynamically adapting to user feedback and de-emphasizing over-consumed content, it seeks to improve user satisfaction and retention in competitive media markets. ### What does this patent NOT cover? Does not cover recommendation systems that do not reduce the influence of frequently consumed media items in the user profile. **Full plain-English explainer:** https://patentbrief.org/patent/us/12118030/dynamic-feedback-in-a-recommendation-system **Original patent:** https://patents.google.com/patent/US12118030 --- _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 Music Apps Learn What You Don't Want in Playlists](https://patentbrief.org/patent/us/12277178/media-content-item-recommendation-system) — This patent describes how a music streaming service learns what kinds of songs or artists a user dislikes for their playlists by tracking what they repeatedly ignore, then uses that information to avoid recommending similar things in the future. - [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. - [How Computers Automatically Tag Movies and Shows Better](https://patentbrief.org/patent/us/12135756/content-recommendation-system-with-weighted-metadata-annotations) — This patent describes a system for automatically improving how movies, TV shows, and other media are tagged with descriptive labels by comparing them to similar content using a special ratio. - [How Facebook's News Feed Picks Stories You'll Like](https://patentbrief.org/patent/us/8171128/facebook-social-graph) — Facebook's 2012 patent explains how it creates a personalized news feed by showing stories about friends' actions, ordered by your interest, and updating it based on what you click. - [How Streaming Services Automatically Build Custom Radio Stations](https://patentbrief.org/patent/us/10108619/microsoft-edge-browser) — A method for streaming services to create custom music radio stations by analyzing genre percentages and artist relationships to pick the best songs.