Recommendation Engine Metadata Matching for Content Personalization
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Solution Overview
Problem
Existing content targeting systems face challenges in scaling with increasing target segments, requiring manual management of segment targeting rules and relying heavily on machine-learning models that are data-intensive and delay-prone, while also lacking control for content publishers and supporting newer channels of content delivery.
Innovation Solution
A system and method for delivering personalized content based on matching user profiles with content metadata, using a recommendation engine that determines content channels and delivers assets based on metadata rules, allowing for automatic content matching and ranking without the need for extensive segment management or large training datasets, providing control to content publishers and supporting multiple delivery channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conditional segment targeting rules are used to target content to user segments, then content personalization can be achieved, but the system becomes increasingly difficult to manage as the number of target segments increases
Solution Approach 1:
The patent replaces the mechanical system of manual segment rule management with an AI-based automated system. The AI model automatically determines content delivery decisions based on user profiles and content metadata, eliminating the need for manual creation and management of conditional segment targeting rules while maintaining personalization effectiveness
Solution Approach 2:
The system enables self-service content personalization where the AI model autonomously makes content delivery decisions without requiring manual intervention for segment management. The system automatically adapts to new content and user profiles, performing self-updating and self-optimization of content targeting
2Extent of automation
If machine-learning models are used to determine content delivery, then control is deferred to the computer system, but the quality of recommendations relies heavily on the amount of trial data available for training
Solution Approach 1:
The patent applies preliminary action by pre-defining content metadata schemas and profile attribute structures before deployment. This allows the system to begin making recommendations with minimal training data, as the framework and evaluation criteria are already established, reducing the dependency on large volumes of trial data for initial model performance
Solution Approach 2:
The system implements dynamic adaptation where the AI model continuously learns from new data and adjusts its recommendations. The model evolves over time as more data becomes available, allowing it to improve reliability dynamically rather than relying solely on initial training data volume
3Extent of automation
If machine-learning models are used for content targeting, then automated decisions can be made, but the system suffers from inherent delay in learning from and responding to aggregate user behavior
Solution Approach 1:
The patent applies local quality by making content delivery decisions at the individual user level rather than waiting for aggregate data analysis. The AI model evaluates user profiles and content metadata in real-time for each content delivery decision, enabling immediate personalized responses without waiting for batch processing of aggregate user behavior data
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for delivery of content based on matching of user profiles with content metadata. The system enables delivery of personalized content, without the overhead of managing segment targeting rules, while providing content publishers or marketers with complete control over such personalization. A recommendation service or application program interface, provided by a computer, cloud computing environment, or other type of computer system, enables receipt and processing of requests, from client devices, for personalized content. A recommendation engine delivers content assets in response to a request from a client device. The recommendation engine determines a content channel and a user identity associated with the request, and then delivers content assets based on rules governing the matching of content asset metadata with the user profile. While content classification evolves over time, so also does the personalization of delivered content.