Tailored Highlight Playlists Based on Viewing Recency
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Solution Overview
Problem
Existing media guidance systems fail to account for the recency of a user's previous viewing of plot-driven programming, indiscriminately refreshing users regardless of how long ago they last watched related content.
Innovation Solution
A media guidance application determines the time since a user last watched related media assets and generates or selects a playlist of highlights based on that time, using predefined or customized clips to refresh the user's memory before accessing a new media asset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stock video clips of related programming are played indiscriminately to refresh user recollection, then the user is updated on previous plots, but the system does not account for the recency of the user's viewing and provides the same refresh regardless of how long ago the user watched the content
Solution Approach 1:
The system dynamically adjusts the refresh content based on the time elapsed since the user's last viewing. Instead of using a static, one-size-fits-all approach, the playlist generation adapts to the specific recency of the user's viewing history, providing different levels of plot summary based on whether the user watched recently or long ago.
Solution Approach 2:
The system changes the parameters of the refresh playlist based on the time interval since last viewing. When a long time has passed, the system includes more comprehensive plot summaries and additional context. When viewing was recent, the system provides briefer refreshes, effectively changing the content parameters based on the temporal parameter of viewing recency.
2Reliability
If comprehensive highlight playlists are generated for all previous seasons, then users who haven't watched in a long time are adequately refreshed, but users who watched recently receive excessive information
Solution Approach 1:
The system applies partial action by providing only the necessary amount of refresh content based on viewing recency. For recent viewers, it provides a partial, condensed summary. For long-time absentees, it provides the full comprehensive summary. This avoids the excessive action of giving complete summaries to everyone, thereby reducing unnecessary time consumption for recent viewers.
Solution Approach 2:
The duration and scope of the highlight playlist are dynamically adjusted based on the time since last viewing. The system automatically shortens or lengthens the refresh content to match the user's needs, optimizing the time investment required for plot refresh while ensuring adequacy for each user segment.
3Adaptability or versatility
If the system tracks detailed viewing history to personalize playlists, then tailored refresh content can be provided, but the complexity of the media guidance application increases
Solution Approach 1:
The system uses the user's own viewing history data to automatically generate personalized playlists without requiring manual input or configuration. The application self-adjusts based on the temporal pattern of the user's viewing behavior, providing personalization through automated analysis of existing data rather than through complex user-driven configuration systems.
Solution Approach 2:
The system incorporates feedback from the user's viewing history to continuously improve playlist personalization. By monitoring when users watch content and using that feedback loop, the system automatically refines its understanding of user preferences and viewing patterns, enabling personalization through iterative learning from user behavior data.
Data Source
AI summary
Systems and methods are provided herein for identifying a playlist of highlights to use for refreshing a user on a plot related to a media asset the user has requested to access based on how long it has been since the user last saw related programming. The media guidance application may receive a request from a user to access a media asset and may determine whether the user previously consumed a related media asset to the media asset. The media guidance application may determine whether a period of time between receiving the request and a time when the user previously consumed the related media asset exceeds a threshold period of time. If the period of time does not exceed the threshold, the media guidance application may play back a predefined playlist of highlights, and if it exceeds the threshold, the media guidance application may play back a customized playlist of highlights.