Pseudo-Identifier Embeddings for Unavailable Content Search
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Solution Overview
Problem
Conventional systems face challenges in providing high-quality content recommendations when user accounts request content that is not available in the target content corpus.
Innovation Solution
A Title Engine generates pseudo-identifiers for unavailable content and uses machine learning models to create embeddings for similar content available in the content corpus, identifying and recommending neighbor content based on user interactions and search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems return output indicating absence of requested content, then system simplicity is maintained, but user experience and recommendation quality deteriorate
Solution Approach 1:
The patent introduces pseudo-identifiers as intermediary elements that bridge unavailable content and available substitute content. When content is unavailable, the system generates a pseudo-identifier that acts as a mediator to find similar available content through embedding comparison, thereby improving user experience without directly exposing system complexity
Solution Approach 2:
The system creates pseudo-identifiers as copies or representations of unavailable content. These pseudo-identifiers enable the system to work with unavailable content indirectly by generating embeddings that represent the unavailable content's semantic meaning, allowing recommendation without requiring the actual content to be present
2Measurement precision
If the system generates pseudo-identifiers and uses machine learning models to create embeddings, then content recommendation quality improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing embeddings for available content in the corpus. When a search query arrives, the system leverages these pre-computed embeddings to quickly find similar content, reducing real-time computational complexity while maintaining high similarity accuracy
Solution Approach 2:
The system transforms content into a different parameter space using embeddings. By converting content into vector representations in a high-dimensional space, the system enables efficient similarity computation through mathematical operations on vectors, reducing computational complexity compared to direct content comparison
3Adaptability or versatility
If the system processes and stores user interaction data for training machine learning models, then recommendation personalization improves, but data privacy and security requirements increase
Solution Approach 1:
The system extracts only the necessary interaction patterns and behavioral signals from user data, separating these from personally identifiable information. By taking out only the essential recommendation-relevant features while leaving out sensitive personal data, the system achieves personalization while reducing privacy risks
Solution Approach 2:
The system uses interaction data as an intermediary to infer user preferences without directly processing or storing sensitive personal information. The interaction patterns serve as mediators that capture user behavior for personalization purposes while maintaining a boundary between raw personal data and processed recommendation signals
Data Source
AI summary
Various embodiments of a Title Engine generate search results that identify content available in a content corpus in response to receiving a search query for content that is currently unavailable in the content corpus. Rather than returning output merely indicating absence of the requested content set forth in the received search query, the Title Engine identifies various content available in the content corpus that is similar to the search query's requested—but unavailable—content. The Title Engine identifies content in a content corpus that is similar to requested content that has been determined as unavailable. Upon determining unavailability of the requested particular content in the content corpus, the Title Engine generates a pseudo-identifier for the requested particular content. The Title Engine inserts the pseudo-identifier into a sequence of content identifiers. The Title Engine generates an embedding for the pseudo-identifier.