Dynamic Content Tagging via Transfer Learning
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Solution Overview
Problem
The challenge lies in efficiently utilizing and managing vast amounts of user-generated content (UGC) for personalized content delivery, as existing systems struggle to automatically identify, tag, and distribute this dynamic content effectively, leading to inefficiencies and 'cold start' issues, which result in lost time and money.
Innovation Solution
A method and apparatus utilizing content tagging and transfer learning to identify user-generated content, generate user-tag affinity vectors, calculate user-content affinity scores, and select relevant content elements for personalized delivery, enabling real-time personalization and adaptation to a changing content pool without frequent model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to identify and tag user-generated content, then productivity is improved, but adaptability deteriorates due to inability to accommodate dynamic and rapidly changing content pools
Solution Approach 1:
The patent implements a dynamic content tagging system where tags are not statically predefined but are generated and updated in real-time based on incoming content characteristics. The system continuously adapts its tagging vocabulary and classification schemes as new content types and trends emerge, allowing the automated system to maintain both high productivity and adaptability to changing content pools.
2Adaptability or versatility
If manual identification and tagging of content is performed, then adaptability is improved, but productivity deteriorates due to high costs and inefficiency
Solution Approach 1:
The patent introduces an intermediary layer between manual tagging and automated processing. This layer uses semi-automated tag suggestion systems, pre-trained models that can be quickly retrained, and hybrid approaches where automated systems handle high-volume routine tagging while human experts focus on defining new tag categories and reviewing complex cases. This intermediary approach maintains adaptability through human oversight while dramatically improving processing speed and productivity.
3Adaptability or versatility
If frequent model updates are performed to accommodate new content, then adaptability is improved, but loss of time and money increases due to cold start issues and update overhead
Solution Approach 1:
The patent implements preliminary action by pre-training models on diverse, representative content samples that cover potential future content types. The system pre-establishes tag hierarchies, classification frameworks, and affinity vector structures that can accommodate new content without requiring complete model retraining. This preliminary preparation allows the system to rapidly adapt to new content through incremental updates rather than frequent cold starts.
Solution Approach 2:
The patent utilizes parameter changes by allowing the system to dynamically adjust model parameters, tag weights, and affinity scores based on incoming content characteristics without retraining the entire model. The system can modify local parameters and weighting schemes to accommodate new content types, achieving adaptability through parameter adjustment rather than full model updates, thereby reducing time and computational resource losses.
Data Source
AI summary
Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.