Set-Top Box Scene-Level Media Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional media recommendation systems in broadcast environments, such as cable TV, typically recommend content based on entire programs rather than specific scenes or user interests, leading to inaccurate targeting of individual user preferences, especially when multiple users share the same set-top box.
Innovation Solution
A method that processes broadcast information by receiving content information synchronized with TV program scenes, determining media data highly correlated to the target scene, and presenting it to users, using a set-top box that calculates correlations between media data and scene content, with user profile weights to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If media recommendation is based on entire programs, then the recommendation system is simple to implement, but the accuracy of targeting individual user preferences deteriorates
Solution Approach 1:
The patent segments the broadcast program into multiple scenes using scene detection technology. Each scene is analyzed independently for content features (objects, actions, semantics) and matched against user profiles. This segmentation allows the system to recommend media based on specific scenes that match user interests rather than requiring the user to watch entire programs, thereby improving recommendation accuracy without significantly increasing system complexity.
2Measurement precision
If scene-level content analysis is implemented, then the accuracy of media recommendation improves, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary scene detection and content feature extraction during broadcast transmission. Scene boundaries and content features (objects, actions, semantics) are pre-analyzed and stored as metadata alongside the broadcast content. When a user requests recommendations, the system simply queries the pre-processed scene data against the user profile, avoiding the need for real-time complex analysis and reducing processing complexity while maintaining high recommendation accuracy.
3Adaptability or versatility
If multiple users share the same set-top box, then device utilization is improved, but the ability to provide personalized recommendations deteriorates
Solution Approach 1:
The system maintains separate user profiles with individual viewing histories and preference characteristics for each user. When multiple users share the set-top box, the system detects which user is currently viewing (through user identification or viewing behavior analysis) and applies scene-based recommendation only to that specific user's profile. This allows the same device to provide highly personalized recommendations to different users by applying local quality differentiation to each user's recommendation experience.
Data Source
AI summary
According to the technical solution of an embodiment of the present invention, since content information is transmitted synchronously with the program scene, within the broadcast stream of data, media data, for example, advertisements, which may be of interest to the user, can be analyzed by selecting scenes at a granular level from the broadcast information. In one regard, the selected scene is being played currently, enhancing the relevance of the real-time analysis. In another regard, selecting a reduced granularity avoids statistical information overshadowing individual information. In this way, the accuracy of selecting media data with respect to a current user is improved.