Cross-Domain Content Recommendation Using User Embeddings
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Solution Overview
Problem
Existing media content providers struggle to efficiently recommend content from one domain to users who have limited familiarity with that domain without requiring extensive user input or processing power.
Innovation Solution
A system uses a neural network to generate embeddings based on user data from one domain (e.g., music) to recommend content from another domain (e.g., podcasts) by analyzing user listening history, demographics, and metadata, reducing the need for user input and processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content recommendation systems are used to recommend content from a domain users have not consumed, then the system requires extensive user input and processing power, but the recommendation accuracy and relevance deteriorate
Solution Approach 1:
The patent introduces embeddings as an intermediary representation that bridges the gap between user consumption history in one domain and content recommendations in another domain. The embedding layer transforms user data and content data into a shared vector space, enabling cross-domain recommendations without requiring extensive direct user input about the target domain.
Solution Approach 2:
The system changes the parameter representation from raw user data and content metadata to transformed embedding vectors. This parameter transformation allows the system to capture complex user preferences and content characteristics in a condensed form that can be efficiently processed and compared across different content domains.
2Productivity
If cross-domain content recommendation is implemented without domain-specific user data, then the processing requirements are reduced, but the recommendation relevance to user preferences deteriorates
Solution Approach 1:
The patent creates a universal embedding space that can handle multiple content domains (music, podcasts, videos, etc.) simultaneously. The same embedding architecture and processing pipeline works across different domains, enabling the system to efficiently process cross-domain recommendations while maintaining relevance through learned user preferences from their consumption history in any domain.
3Measurement precision
If extensive user input is collected to improve cross-domain recommendation accuracy, then the recommendation quality improves, but the user interaction burden and data processing requirements increase
Solution Approach 1:
The system enables self-service recommendation by automatically inferring user preferences for content domains they haven't explicitly consumed from their consumption history in other domains. The embedding-based model performs this inference without requiring users to provide explicit input about their preferences in the target domain, thus maintaining high recommendation quality while minimizing user interaction time.
Data Source
AI summary
A method includes displaying, at a device of a first user, a user interface that includes:representations of content items of a first content domain that the first user has consumed; and an affordance for viewing recommended content items of a second content domain, distinct from the first content domain. The method includes obtaining, for each respective content item of a plurality of content items of the second content domain, consumed by at least one user other than the first user, information for the respective content item, and determining a first content item of the plurality of content items. The method includes, in response to a user input selecting the affordance for viewing the recommended content items of the second content domain, displaying at the device of the first user, information that corresponds to the first content item of the plurality of content items of the second content domain.