Media Selection Interface with Adjustable Attribute Ratings
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Solution Overview
Problem
Existing methods for selecting content assets, such as those on YouTube or Netflix, are inefficient due to the abundance of irrelevant results from simple search techniques, requiring users to go back and forth to refine their searches, which is time-consuming and not user-friendly for the general public.
Innovation Solution
A method and apparatus that allow users to select items based on attributes with adjustable ratings, enabling users to refine their selections directly within the context of browsing, using a processor and interface to display attributes and allow rating adjustments, thereby selecting similar items based on user preferences without leaving the navigation context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple text entry search is used, then ease of operation is improved, but measurement precision of search results deteriorates
Solution Approach 1:
The system pre-defines multiple attributes for each content item (genre, director, actor, duration, etc.) and assigns default importance ratings. This preliminary structuring of data allows the system to immediately perform refined filtering without requiring users to manually specify each parameter, thus maintaining ease of operation while improving search precision.
Solution Approach 2:
The system dynamically adjusts the importance ratings of different attributes based on user interactions and feedback. By changing the parameters (attribute weights) in real-time, the system can refine search results to better match user preferences without requiring users to understand complex search syntax or repeatedly adjust multiple parameters manually.
2Measurement precision
If complex input means with explicit preferences are used, then measurement precision of search results is improved, but device complexity increases
Solution Approach 1:
The system pre-configures multiple content attributes (genre, director, actor, duration, language, etc.) with default importance ratings before the user performs any search. This preliminary structuring allows users to immediately benefit from refined search results without needing to understand or configure complex parameters, thus reducing input complexity while maintaining high measurement precision.
Solution Approach 2:
The system automatically manages the complexity of attribute weighting and search parameter optimization without requiring user intervention. It self-adjusts based on implicit feedback from user behavior patterns, thereby providing precise search results while keeping the interface simple and the input process straightforward for users.
3Measurement precision
If implicit input means like browsing tracking are used, then measurement precision of user preferences is improved, but loss of time increases
Solution Approach 1:
The system pre-structures all content items with multiple attributes and default importance ratings before any user interaction occurs. This preliminary preparation allows the system to immediately generate accurate recommendations based on user feedback without requiring time-consuming data collection or analysis phases, thus reducing time loss while maintaining preference accuracy.
Solution Approach 2:
The system uses explicit user feedback (ratings, likes, dislikes) combined with the pre-structured attribute system to quickly refine and accurate user preferences. This feedback mechanism, combined with the pre-prepared attribute framework, enables rapid convergence to accurate preference modeling without the time delays associated with implicit tracking methods.
4Measurement precision
If users must go back to refine search requests, then measurement precision of search results is improved, but loss of time increases
Solution Approach 1:
The system pre-defines multiple content attributes with importance ratings that are immediately applicable to search results. Users can directly adjust these pre-configured attributes to refine their search without needing to navigate back to previous screens or re-enter search parameters, thus maintaining high measurement precision while minimizing time loss.
Solution Approach 2:
The system adds a new dimension to search refinement by allowing users to adjust attribute importance ratings directly within the search results context rather than requiring navigation to separate configuration screens. This dimensional shift in the user interface allows simultaneous viewing of results and refinement controls, eliminating back-and-forth navigation time.
Data Source
AI summary
Items having a plurality of attributes associated therewith, each of said associated attributes having a rating, the rating indicating the importance of the attribute to the user can be selected (201) and displayed (205) with its associated attributes and corresponding ratings. The ratings can be adjusted (207) and at least one item similar to one of the selected at least one first item is selected (209) based on the rating of at least one of the displayed attributes.