AI Channel Recommendations for Content Access Level Conversion

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Solution Overview

Problem

Content sharing platforms generate broadly targeted recommendations for content access levels, which are often ignored by channel owners due to their lack of perceived value, leading to wasteful consumption of computing resources.

Innovation Solution

An AI model trained on channel-related features predicts the number of new users likely to obtain a content access level within a certain time, providing personalized and targeted recommendations to channel owners.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If broadly targeted recommendations for content access levels are generated, then coverage of channel owners is improved, but conversion rate deteriorates due to lack of perceived value

Engineering Contradiction:

Improvecoverage of channel ownersVSAvoidconversion rate

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system transitions from uniform broad recommendations to personalized recommendations tailored to each channel owner's specific characteristics. The AI model analyzes individual channel features such as content type, audience demographics, and engagement metrics to generate customized recommendations that are locally optimized for each channel, thereby improving both coverage and conversion rate simultaneously

Inventive Principle:

Principle #3Local quality

2Productivity

If broadly targeted recommendations are generated, then resource utilization is improved, but computing resource waste increases due to low engagement

Engineering Contradiction:

Improveresource utilizationVSAvoidcomputing resource waste

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of channel features and user characteristics before generating recommendations. The AI model pre-processes and evaluates multiple channel attributes in advance, identifying only those channels and recommendation types that have high probability of conversion. This preliminary filtering action prevents wasteful generation and delivery of low-value recommendations, optimizing computing resource utilization while reducing energy loss

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12556754B2Systems and methods for generating content sharing platform recommendations using machine learning

Publication Date: 2026.02.17 GOOGLE LLC

AI summary

A method includes identifying a channel associated with a user of a content sharing platform. An indication of one or more channel related features associated with the channel is provided as input to an artificial intelligence (AI) model. The AI model is trained to predict a number of new users to obtain at least one content access level for the channel within a predetermined time of an effective date of the at least one content access level for the channel. One or more outputs of the AI model is obtained. The one or more obtained outputs comprise a predicted number of new users to obtain at least one content access level for the channel within a predetermined time of an effective date of the at least one content access level for the channel. A recommendation is provided for presentation on a client device of the user.