Dynamic Content Recommendation System Using Playback Duration
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Solution Overview
Problem
Existing recommendation systems primarily focus on recommending similar or correlated content based on user preferences and content metadata, lacking the ability to suggest anti-correlated content effectively, especially in contexts like in-vehicle entertainment systems where user preferences may change over time.
Innovation Solution
A recommendation system that utilizes a server with a processor and memory to generate both correlated and anti-correlated content recommendations based on the elapsed duration of the current content being played, allowing for dynamic switching between types of recommendations depending on thresholds, such as displaying anti-correlated content at the beginning and correlated content towards the end of media playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation systems only recommend correlated content based on user preferences and content metadata, then the system maintains consistency with user preferences, but the system lacks the ability to suggest anti-correlated content effectively
Solution Approach 1:
The patent applies dynamics by making the recommendation type change over time based on playback duration. The system transitions from recommending anti-correlated content at the beginning of playback to correlated content toward the end, allowing the recommendation behavior to adapt dynamically rather than remaining static throughout the entire content playback period.
Solution Approach 2:
The patent segments the content playback experience into different phases: an initial phase where anti-correlated content is recommended and a later phase where correlated content is recommended. This segmentation allows the system to provide different types of recommendations at different times, increasing versatility without overwhelming complexity.
2Productivity
If the system recommends similar content throughout playback, then user preferences are consistently satisfied, but user engagement may decrease when preferences change over time
Solution Approach 1:
The patent implements periodic action by changing the recommendation strategy at specific intervals during content playback. The system switches from anti-correlated recommendations in the early period to correlated recommendations in the later period, creating a rhythmic pattern of recommendation types that adapts to changing user engagement and preferences throughout the viewing experience.
3Productivity
If the system switches between anti-correlated and correlated content recommendations, then user engagement is enhanced, but the complexity of determining when to switch increases
Solution Approach 1:
The patent applies parameter changes by using playback duration as the key parameter to determine when to switch between recommendation types. Instead of complex analysis of user behavior or content characteristics, the system uses the simple, easily measurable parameter of elapsed playback time to trigger the transition from anti-correlated to correlated recommendations, reducing the difficulty of determining switch timing.
Data Source
AI summary
Systems and methods are described herein for providing content recommendations to a user based on an elapsed playback time of the current content on the user device. A recommendation server having memory and a processor can be used to compare an elapsed playback time of the current content and compare the elapsed playback time with one or more threshold values. If the elapsed playback time is less than a first threshold, anticorrelated content with respect to the current content can be recommended and presented on or using the user device.