Personalized Content Recommendations with LSTM Preference Updates
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Solution Overview
Problem
Conventional media recommendation systems fail to adapt to changing user preferences and often provide content recommendations that include scenes or portions users prefer to skip, requiring users to filter through unnecessary content.
Innovation Solution
A recommendations engine uses a trained model that is personalized based on content consumption data, including user activity and metadata, to generate tailored content recommendations, utilizing a long short-term memory recurrent neural network (LSTM RNN) to optimize and update recommendations based on user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation systems provide content recommendations based on previously consumed content, then users can find similar content they may be interested in, but the system fails to adapt to changing user preferences
Solution Approach 1:
The patent implements a dynamic recommendation system that continuously updates user preference models as new consumption data becomes available. The system transitions from static historical analysis to dynamic adaptive modeling, where the recommendation engine recalibrates user preferences in real-time based on recent viewing behavior, thereby resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The system incorporates feedback loops where user consumption patterns are continuously monitored and fed back into the recommendation model. This feedback mechanism allows the system to learn from user interactions and adjust recommendations accordingly, ensuring both adaptability to changing preferences and maintained recommendation accuracy through validated learning cycles.
2Productivity
If conventional recommendation systems provide content recommendations similar to consumed content, then users can discover related content, but the recommendations include scenes or portions users prefer to skip
Solution Approach 1:
The patent extracts and isolates specific portions of content that users have indicated interest in, separating these preferred segments from the overall content stream. By extracting only the relevant portions that match user preferences and removing unwanted scenes, the system improves content discovery efficiency while eliminating the need for users to manually filter through irrelevant content.
Solution Approach 2:
The system applies local quality enhancement by customizing recommendations at the scene or segment level rather than treating content uniformly. It identifies and highlights specific high-value portions within content based on user preferences, allowing users to efficiently discover interesting segments without wading through unrelated material, thereby reducing filtering effort while maintaining discovery productivity.
Data Source
AI summary
Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.