Interest Based Row Selection for Digital Content
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Solution Overview
Problem
Conventional digital content distribution systems face inefficiencies in recommending digital media to users due to the need for large data storage and management to determine similar user preferences, making personalized recommendations impractical in some situations.
Innovation Solution
A method that involves receiving user preferences, determining preference tags, comparing them to metadata tags for genres, computing scores for each genre, sorting, and selecting genres to recommend based on user interests, thereby enhancing content display relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital content distribution systems use conventional methods to determine similar user preferences for personalized recommendations, then user experience is enhanced, but large amounts of data need to be stored and managed making the approach inappropriate in some situations
Solution Approach 1:
The patent extracts and utilizes only the essential preference attributes of the user (e.g., genre preferences, actor preferences, director preferences) rather than storing and processing complete user profiles or viewing histories. This extraction approach enables personalized recommendations while minimizing data storage requirements by focusing only on the critical preference tags needed for matching.
Solution Approach 2:
The system transforms user preferences into a standardized parameter format using preference tags that can be directly compared with content metadata tags. This parameter transformation allows for efficient matching without requiring complex data structures or large datasets, converting subjective user preferences into objective, comparable parameters.
2Measurement precision
If digital content distribution systems store and manage large amounts of user data to determine similar users, then more accurate personalized recommendations can be made, but the complexity of data management increases
Solution Approach 1:
The patent segments user preferences and content attributes into discrete, independent preference tags (e.g., genre tags, actor tags, director tags). This segmentation allows for simplified comparison and matching operations, reducing data management complexity while maintaining recommendation accuracy by treating each preference attribute as an independent unit that can be efficiently processed.
Solution Approach 2:
The system changes the representation of user preferences and content attributes into a standardized tag-based parameter system. This parameter transformation simplifies the matching process by converting complex user profiles and content metadata into comparable tag sets, reducing computational complexity while preserving the essential information needed for accurate recommendations.
3Measurement precision
If digital content distribution systems use comprehensive user data for recommendations, then user preferences can be more accurately determined, but the system becomes less adaptable to situations with limited data storage
Solution Approach 1:
The patent extracts only the essential preference attributes needed for effective recommendations, discarding redundant or less important data. This extraction enables the system to achieve accurate user preference determination with minimal data storage, thereby adapting to environments with limited storage capacity while maintaining recommendation quality.
Solution Approach 2:
The system transforms user preferences into a compact parameter representation using preference tags that can be efficiently stored and processed. This parameter transformation allows the system to adapt to various storage environments by providing an efficient data structure that maintains accuracy while minimizing storage requirements.
Data Source
AI summary
Genres and associated digital items are recommended to a user based on the interests of the user. Genres that are of interest to the user are determined based on user preferences gathered implicitly or explicitly. The genres are then scored and sorted based on different scoring and sorting techniques. A subset of the scored and sorted genres is then selected for recommending to the user.