Simulation Engine for Adaptive Content Recommendation Feedback

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Solution Overview

Problem

Users face difficulty in selecting television programming due to the vast number of channels and content options, making it hard to decide on a program to view from the grid guide.

Innovation Solution

A system and method that utilizes a recommendation algorithm to generate content recommendations based on user viewing data, allows user feedback on these recommendations, and modifies the algorithm accordingly to improve suggestion accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a wide variety of content channels are provided to users, then content diversity is improved, but user difficulty in selecting content increases

Engineering Contradiction:

Improvecontent diversityVSAvoidcontent selection difficulty

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system implements feedback loops where user interactions with recommended content (viewing behavior, ratings, preferences) are continuously collected and used to refine the recommendation algorithm. This allows the system to adapt to individual user preferences while managing the complexity of content selection across diverse channels

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The recommendation algorithm automatically generates personalized content suggestions based on user viewing data without requiring manual user input for each recommendation. The system self-adjusts by processing user behavior patterns and autonomously providing tailored content selections

Inventive Principle:

Principle #25Self-service

2Ease of operation

If a recommendation algorithm is implemented to help users select content, then content selection ease is improved, but system complexity increases

Engineering Contradiction:

Improvecontent selection easeVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The recommendation algorithm serves as an intermediary layer between the user and the vast content library. It processes user preferences and viewing data to generate personalized recommendations, shielding users from the complexity of navigating numerous channels while managing the complexity through structured data processing

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If user feedback is collected and used to modify the recommendation algorithm, then recommendation accuracy is improved, but data processing requirements increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoiddata processing requirements

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system collects comprehensive user data including viewing behavior, ratings, and preferences to ensure high recommendation accuracy. By gathering more data than minimally required and processing it through the algorithm, the system achieves precise personalization while managing data volume through efficient processing methods

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10055746B1Method and system for obtaining feedback for a content recommendation by various algorithms

Publication Date: 2018.08.21 DIRECTV LLC

AI summary

A simulation engine and method of operating a simulation system includes a plurality of user devices and an interface receiving user device data and a recommendation request from at least one the plurality of user devices. The system also includes a memory storing the user device data therein. The system also includes a recommendation engine that generates a content recommendation based on the user device data and an algorithm. The interface communicates the content recommendation to the user device. The recommendation engine receives a recommendation rating from at least one of the plurality of user devices and changes the algorithm in response to the recommendation rating. The user device receives a different recommendation of the changed algorithm within the simulation engine.