Feature Distribution Management in Content Recommendation Systems

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Solution Overview

Problem

Content recommendation systems face performance degradation due to biased features in content catalogs, where a large number of content items are tagged with specific features, leading to increased response times and inaccurate recommendations.

Innovation Solution

A method to manage feature distribution by determining when the number of content items tagged with a feature exceeds a threshold, taking corrective action such as modifying user feature vectors and scoring functions to ignore or de-tag biased features, thereby improving system performance and accuracy of recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a large number of content items are tagged with specific features to improve recommendation coverage, then the system can provide more comprehensive recommendations, but the response time increases and system performance degrades

Engineering Contradiction:

Improverecommendation coverageVSAvoidresponse time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system pre-calculates and stores feature distributions and content scores in advance, so that when a recommendation request arrives, the pre-computed data can be quickly retrieved and used without performing heavy computations in real-time, thus reducing response time while maintaining comprehensive recommendation coverage

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically monitors feature distribution and identifies biased features without manual intervention, then self-corrects by adjusting or removing these features from the recommendation process, eliminating the need for manual feature management while maintaining system performance

Inventive Principle:

Principle #25Self-service

2Productivity

If biased features are included in the recommendation algorithm to improve relevance, then more popular content can be recommended, but the accuracy of personalized recommendations decreases

Engineering Contradiction:

Improverecommendation volumeVSAvoidrecommendation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system applies different treatment to different features: biased features are detected and either removed or down-weighted, while non-biased features are retained and used for personalized recommendations. This selective approach ensures that only high-quality, accurate features contribute to the recommendation precision

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system continuously monitors feature distribution and detects when features become biased through feedback mechanisms. When bias is detected, the system automatically adjusts the feature weights or removes them, creating a closed-loop system that maintains recommendation accuracy over time

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250008176A1Managing feature distribution

Publication Date: 2025.01.02 THINKANALYTICS

AI summary

A method of managing feature distribution in content of a content catalog for a content distribution system having a plurality of users is provided. The method comprises taking corrective action responsive to determining that a number of content items in a content catalog tagged with a feature exceeds a threshold.