Media Playlist Recommendations Using Child Viewer Disinterest Feedback
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Solution Overview
Problem
Parents struggle to create playlists for their children that avoid content the children will object to, leading to frequent requests for new content and disruption of activities due to the vast array of available media and rapidly changing child preferences.
Innovation Solution
An interactive media guidance application that receives expressions of disinterest from children, updates their user profiles based on these disinterests, and recommends media assets less likely to be objected to, using a comprehensive set of parameters to identify suitable content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parents manually select media assets for children's playlists, then they can control content approval, but the process becomes time-consuming and disruptive due to frequent requests for new content
Solution Approach 1:
The system enables children to self-select content by automatically generating playlists based on their viewing history and preferences. The automated recommendation system serves the children's content selection needs without requiring continuous parent intervention, thereby reducing the time parents spend on content approval while maintaining reliable content selection.
Solution Approach 2:
The system incorporates feedback mechanisms where children's viewing activity and preferences are continuously monitored and used to update their profiles. This feedback loop allows the system to automatically adjust recommendations based on actual child preferences, reducing the need for manual content selection and minimizing time loss.
2Adaptability or versatility
If parents continuously add new content to playlists, then children have more viewing options, but this increases disruption to current activities and causes frustration
Solution Approach 1:
The system performs preliminary content selection and playlist generation based on children's historical viewing data and predicted preferences. By pre-selecting appropriate content before children request it, the system provides content variety without causing disruption, as the content is already prepared and tailored to match children's interests.
Solution Approach 2:
Children's content preferences are automatically captured and used to generate personalized playlists without parent intervention. This self-service approach maintains content variety and adaptability while eliminating the frustration and disruption caused by continuous manual content addition and frequent requests for new content.
3Measurement precision
If the system monitors children's viewing activity to improve recommendations, then content accuracy improves, but this requires tracking and processing of viewing data
Solution Approach 1:
The system automatically captures and processes viewing data without requiring complex manual input mechanisms. Children's viewing activity is passively monitored and automatically used to update their profiles and generate recommendations, achieving high measurement precision while keeping the data processing complexity manageable through automated background processing.
Solution Approach 2:
The system continuously monitors viewing activity and uses this feedback to refine recommendations. By implementing a feedback loop where viewing data is automatically processed and used to update child profiles, the system achieves high accuracy in preference detection without requiring overly complex data processing infrastructure.
Data Source
AI summary
Systems and methods for providing a first user with recommendations of media assets for inclusion in a playlist for a second user based on the second user's viewing activity. These systems and methods receive, from the second user, an expression of disinterest in a media asset included in the playlist for the second user, update a user profile associated with the second user based on the expression of disinterest, and determine a recommendation for another media asset based on the updated user profile associated with the second user. The systems and methods provide the recommendation to the first user. By recommending media assets that the second user is least likely to object to, these systems and methods reduce the frequency of disruptive requests for media assets from the second user.