Real-Time Media Recommendations Using Reinforcement Learning

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Solution Overview

Problem

Existing media recommendation systems fail to provide real-time updates based on users' browsing and navigation actions during a session, leading to less accurate and less engaging content personalization.

Innovation Solution

Implement a reinforcement learning model that collects real-time user input and updates media asset recommendations during the browsing session, using rewards based on user interactions to optimize content presentation and engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If offline batch processing is used to update recommendations, then system complexity is reduced, but recommendation accuracy and timeliness deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidrecommendation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent transitions from static offline batch processing to dynamic real-time processing. The system continuously updates recommendations during user browsing sessions by processing user actions as they occur, making the recommendation system adaptive and responsive to current user behavior rather than relying on outdated batch-processed data.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent implements a feedback mechanism where user actions during browsing sessions are immediately processed and fed back into the recommendation system. This closed-loop feedback allows the system to learn from user interactions in real-time and adjust recommendations accordingly, improving accuracy without requiring complex offline batch processing architectures.

Inventive Principle:

Principle #23Feedback

2Speed

If real-time processing is implemented, then recommendation timeliness is improved, but computational resource consumption increases

Engineering Contradiction:

Improverecommendation update speedVSAvoidcomputational resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The patent applies partial processing by focusing computational resources on processing only the most recent and relevant user actions during browsing sessions, rather than processing all historical data in real-time. This selective processing approach achieves timely recommendations while managing computational resource consumption more efficiently.

Inventive Principle:

Principle #16Partial or excessive action

3Loss of information

If comprehensive user action collection is implemented, then information completeness is improved, but data processing complexity increases

Engineering Contradiction:

Improveuser action information completenessVSAvoiddata processing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent extracts and processes only the essential user actions that are most relevant to recommendation accuracy, such as browsing behavior, selections, and engagement metrics. By selectively extracting key information rather than processing all possible user data, the system achieves comprehensive information collection while managing data processing complexity through focused extraction of critical signals.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250260871A1Content personalization based on reinforcement learning

Publication Date: 2025.08.14 ADEIA GUIDES INC

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.