Media Fast-Forward Recommendations From Rewind Behavior
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Solution Overview
Problem
Current methods for fast forwarding and rewinding media assets require manual operations, leading to inaccurate skipping or rewinding of segments, as users are unaware of the optimal length to skip or rewind, often resulting in missed interesting content or prolonged viewing time.
Innovation Solution
A system that analyzes user rewind patterns and metadata to identify characteristics of interest, automatically adjusting fast forwarding or recommending against skipping segments containing those characteristics, using machine learning and AI to adapt to user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual fast forward or rewind operations are used, then users can control playback position, but users may fast forward or rewind too much or too little and miss interesting content or prolong viewing time
Solution Approach 1:
The system performs preliminary analysis of media content to identify interesting segments before the user reaches them. By pre-processing the content and marking segments of interest, the system prepares information that guides the user's fast-forward decisions, ensuring they don't skip important content while still achieving time savings.
Solution Approach 2:
The system provides feedback to the user about upcoming interesting segments, allowing the user to make informed decisions about whether to fast forward. This feedback mechanism includes notifications or visual indicators that inform users about content they might want to watch, preventing them from accidentally skipping valuable segments.
2Loss of time
If users manually fast forward to save time, then viewing time is reduced, but users are unaware of the optimal length to skip and may miss interesting content
Solution Approach 1:
The system analyzes and identifies interesting segments in advance of when the user needs to make fast-forward decisions. By pre-processing content and marking segments of interest, the system provides users with information about what they might want to watch before they actually reach those points in the media.
Solution Approach 2:
The system provides real-time feedback to users about upcoming interesting segments, enabling them to make informed decisions about whether to fast forward. This feedback includes notifications or visual indicators that alert users to content worth watching, preventing accidental skipping of valuable segments while still allowing time-saving fast-forwarding of uninteresting portions.
3Loss of time
If the system automatically fast forwards segments, then viewing time is further reduced, but the system may fast forward segments that contain content the user would have wanted to watch
Solution Approach 1:
The system allows users to train it by providing feedback on segments they find interesting or uninteresting. This self-service mechanism enables the system to learn and adapt to individual user preferences over time, improving the accuracy of automatic fast-forward decisions without requiring complex pre-programming of user preferences.
Solution Approach 2:
The system dynamically adjusts its fast-forward behavior based on learned user preferences and contextual information. By changing parameters such as the aggressiveness of fast-forwarding or the thresholds for identifying interesting segments, the system can adapt its behavior to balance time-saving with content preservation based on individual user needs.
Data Source
AI summary
Systems and methods for providing fast forwarding recommendations based on the user's consumption history are disclosed. The consumption history includes data relating to attributes that were previously rewinded and watched and those that were skipped and forwarded. It also includes scores for attributes that were present and absent in a portion that was previously rewinded or forwarded. A score is assigned to the attributes and used for determining a consumption pattern. If the consumption pattern indicates that the user previously rewinded and watched the attribute, then a recommendation not to skip an upcoming portion that includes the attribute is provided. A graphical timeline that depicts the amount of time saved by skipping the portion of the media asset with the attribute is also provided.