Content Search Suggestions Through Cross-Domain Neural Projection
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Solution Overview
Problem
Content providers face challenges in accurately evaluating, categorizing, and organizing vast libraries of content due to the increasing amount of metadata and intricate relationships between content items, which complicates the provision of better consumer experiences.
Innovation Solution
A system utilizing a content comprehension engine that applies deep neural learning to generate collaborative filtering representations of content items, enabling cross-domain recommendations by mapping embeddings to different content types and computing similarity scores to provide intelligent search results and content item models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If content providers evaluate, categorize, and organize vast libraries of content with increasing metadata, then the amount of information about content items increases, but the complexity of assimilating relationships between content items increases geometrically
Solution Approach 1:
The patent introduces an intermediary system (content comprehension engine with neural projection models) that mediates between the source content item and target content items across different domains. This intermediary transforms embeddings from one domain to another, enabling relationship discovery without direct geometric comparison, thus managing the complexity of assimilating relationships while preserving information.
Solution Approach 2:
The patent applies dimensionality transformation by mapping embeddings from a source domain embedding space to a target domain embedding space using neural projection models. This allows comparison and relationship discovery across different content types by transforming them into a common representational framework, managing the geometric complexity of relationship assimilation.
2Measurement precision
If content providers strive to reveal and understand relationships between content items more accurately, then the accuracy of content relationships improves, but the computational complexity of processing vast libraries increases
Solution Approach 1:
The patent replaces traditional mechanical or rule-based content relationship processing with neural network-based embedding transformation. The neural projection models automatically learn and capture complex relationships between content items across domains, achieving high measurement precision while managing computational complexity through differentiable optimization rather than exhaustive search.
Solution Approach 2:
The patent changes the parameters of content representation by transforming embeddings through neural projection models. By adjusting the embedding dimensions and projection parameters, the system can control the trade-off between relationship accuracy and computational complexity, achieving accurate relationship discovery while managing processing demands.
3Quantity of substance
If the number of network-connected devices and content items increases, then the availability of content increases, but the difficulty of organizing and evaluating content libraries increases
Solution Approach 1:
The patent creates a universal content comprehension engine that can handle multiple content types and domains through a single neural projection framework. This multi-functional system can evaluate, categorize, and organize diverse content libraries by projecting embeddings from different domains into a common space, managing the organizational difficulty despite increasing content quantity.
Data Source
AI summary
Systems and methods for intuitive search and recommendation including a content comprehension engine executing on a computer processor and configured to: receive a recommendation request identifying a source content item; generate a first embedding for the source content item in a first embedding space from content metadata and contextual data; apply a trained neural projection model to map the first embedding to a second embedding space, thereby producing a projected embedding; compute, for content item models stored in a repository, a similarity score between the projected embedding and the content item model, each content item model including word-vector collaborative-filtering representations of an available content item; select, based on the similarity scores, a subset of the content item models; and output a result set including the available content items corresponding to the subset and ordered by the similarity scores.