Reinforcement Learning Recommendations for Real-Time Browsing Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media content recommendation systems fail to account for real-time user browsing and navigation actions, providing outdated recommendations that do not adapt during a browsing session, leading to inaccurate and less engaging user experiences.
Innovation Solution
Implement a reinforcement learning model that collects real-time user input and updates media asset recommendations dynamically during a browsing session, using rewards based on user interactions to optimize content personalization and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media asset recommendations are updated only after a user's browsing session ends (offline manner), then system complexity is reduced and processing load is minimized, but recommendation accuracy and relevance deteriorate because the system cannot adapt to real-time user actions
Solution Approach 1:
The patent implements dynamic recommendation updates by transitioning from a static offline update model to a dynamic real-time update model. The system continuously monitors user actions during browsing sessions and updates media asset recommendations immediately, making the recommendation system adaptive and responsive to changing user preferences rather than relying on batch processing after sessions end
Solution Approach 2:
The patent incorporates feedback mechanisms by using reinforcement learning models that receive continuous feedback from user actions (clicks, views, scrolls, pauses). This feedback loop allows the system to learn from user interactions in real-time and adjust recommendations dynamically, improving accuracy through continuous adaptation rather than periodic updates
2Adaptability or versatility
If comprehensive real-time user action data is collected and processed, then recommendation relevance and user engagement improve, but data processing load and computational resources increase
Solution Approach 1:
The patent implements self-service through automated reinforcement learning models that autonomously process user action data and generate updated recommendations without requiring manual intervention. The system automatically collects, analyzes, and acts on user feedback in real-time, reducing the need for human operators while maintaining high adaptability to user preferences
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant user actions that provide meaningful feedback for recommendation improvement. Rather than processing every single user interaction with equal depth, the system focuses on significant actions (such as selections, extended views, or explicit feedback) while using lighter processing for routine interactions, optimizing computational resource usage
Data Source
AI summary
Methods and systems are provided herein for providing media asset recommendations to a user based on user input in real time and using a reinforcement learning model or media asset recommendation model. The model includes a plurality of states, each having a set of media asset recommendations, a plurality of available user input options, and a state value. During a browsing session, a first set of media asset recommendations is provided to a user based on a current state corresponding to the browsing session. The user then selects a user input option, and a second set of media asset recommendations is determined based on the received user input option. The second set of media asset recommendations is then provided to the user during the browsing session.