Media Recommendation System Multiple Interest Selection
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Solution Overview
Problem
Current content distribution systems only allow users to select one content item from a recommendation list, leading to wasted information on user interest in multiple items and inefficient content discovery in the vast sea of streaming content.
Innovation Solution
The system allows users to select multiple content items for interest indication, using explicit inputs like tagging or placing in a 'watch later' folder, and infers interest through gaze detection and cursor movement, influencing machine learning algorithms for future recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users can only select one content item from recommendation list, then system complexity is reduced, but user interest information is lost and content discovery efficiency deteriorates
Solution Approach 1:
The patent segments the single-selection constraint into multiple independent selection opportunities. Users can select one content item from the current recommendation list, then receive additional recommendation lists with different items. This segmentation allows accumulation of multiple interest signals without requiring complex multi-selection interface in a single view, thus preserving user interest information while maintaining system manageability.
Solution Approach 2:
The system performs preliminary actions by providing multiple recommendation lists in advance, each containing different content items. This allows users to express interest in multiple items across different lists before final selection, capturing broader user preferences early in the interaction process rather than forcing a single immediate choice.
2Productivity
If multiple content items are recommended and user interest tracking is enabled, then content discovery improves, but system complexity increases
Solution Approach 1:
The patent divides the complex task of tracking multiple user interests into manageable segments by organizing recommendations into separate lists. Each list can be processed and tracked independently, reducing the complexity burden on the system while still enabling comprehensive content discovery across multiple items and categories.
Solution Approach 2:
The system implements partial tracking by focusing on capturing user interest signals from multiple recommendation lists rather than attempting to track every possible interaction detail. This partial action approach provides sufficient content discovery improvement without the full complexity overhead of comprehensive multi-dimensional tracking.
3Measurement precision
If recommendation system captures detailed user interest signals, then recommendation accuracy improves, but information processing requirements increase
Solution Approach 1:
The patent segments user interest data collection into discrete events tied to specific recommendation lists and items. Rather than continuously monitoring all user interactions, the system captures interest signals at specific segmentation points (when users view or select from recommendation lists), reducing overall data processing volume while maintaining precise measurement of user preferences at each decision point.
Data Source
AI summary
Systems and methods for recommending media based on received signals indicating user interest in a plurality of recommended media items are presented. In one or more aspects, a system is provided that includes a recommendation component configured to analyze a set of videos and identify a first subset of videos to recommend to a user, wherein respective representations of the videos included in the first subset are presented to a user via a user interface displayed at a client device. The system further includes a selection component configured to receive input regarding user interest in two or more videos included in the first subset of videos. The recommendation component further identifies a first subset of the two or more videos for re-recommending to the user based on the received input.