Taste Graph for Probabilistic Content Compatibility
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Solution Overview
Problem
Existing online content services struggle to recommend compatible digital content items, such as images and videos, as their compatibility is often subtle and difficult to articulate, unlike text-based content, which hinders personalized content aggregation and recommendation systems.
Innovation Solution
A taste graph is generated based on analyses of user-curated content collections, using feature vectors to identify compatible digital content items that align with the user's taste, allowing for probabilistic recommendations of complementary content items for inclusion in user-generated collections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based content analysis methods are used, then content matching precision is improved, but applicability to digital content items (images, videos, audio) deteriorates
Solution Approach 1:
The patent replaces text-based mechanical analysis methods with computer vision and machine learning algorithms that can process visual and audio features of digital content items, enabling the system to analyze images, videos, and audio files with the same precision previously only achievable through text analysis
Solution Approach 2:
The system transforms digital content items into standardized feature vectors by extracting key parameters such as color histograms, texture features, audio spectrograms, and metadata, converting diverse digital content formats into a unified representation that can be processed and compared systematically
2Measurement precision
If user-curated collections are analyzed to determine compatibility, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of user-curated collections to pre-compute compatibility metrics, feature vectors, and compatibility scores for all content items in advance. This preprocessing creates a structured database of compatibility relationships that can be quickly queried without requiring complex real-time analysis
Solution Approach 2:
The patent creates simplified representations (feature vectors) that copy the essential compatibility characteristics of original content items. These compressed feature vectors capture the key attributes needed for compatibility determination without requiring analysis of the full original content, reducing computational complexity while maintaining recommendation accuracy
Data Source
AI summary
According to aspects of the disclosed subject matter, a taste graph comprising likely content collection nodes with corresponding likely digital content items is generated through one or more analyses of a corpus of content collections that is maintained by the online content service. As should be understood, this corpus of content collections is comprised of a plurality of curated content collections, with each content collection comprising a plurality of digital content items. With this taste graph available, as a user generates (or in response to a user generating) a content collection of digital content items, reference can be made to the taste graph to identify one or more digital content items that may be added to the content collection, where the one or more digital content items have a probabilistic likelihood of being complimentary and/or compatible with the other digital content items of the content collection.