Video Delivery Content Ranking via Score Segmentation
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Solution Overview
Problem
Video delivery services face challenges in dynamically generating a user interface that effectively combines and prioritizes advertisement, media program campaign, and recommendation content in real-time, given the limited time available for interface generation and the need to consider various user-specific factors.
Innovation Solution
A content ranking system that communicates with ad, media program campaign, and recommendation engines to generate scores for content relevance and importance, allowing the video delivery service engine to combine these scores to determine the most relevant content to display, thereby optimizing the user interface with a focus on user interaction and impression probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the video delivery service displays the same display content in the same portion of the interface for all users, then the system operation is simple and consistent, but the user interface lacks personalization and relevance to individual user preferences
Solution Approach 1:
The content selection system is segmented into multiple independent components: a content provider that supplies display content, a content selector that chooses specific content, and a video delivery service that presents content to users. This segmentation allows each component to specialize in one function, enabling personalization without overwhelming system complexity.
Solution Approach 2:
A content selector acts as an intermediary between the content provider and the video delivery service. This intermediary component receives multiple content options, applies selection criteria (including user-specific factors), and outputs a single selected content item. The intermediary absorbs the complexity of content evaluation and selection logic, keeping the overall system manageable while enabling personalized content delivery.
2Measurement precision
If the video delivery service aggregates and rates multiple types of content from different engines, then the content relevance and personalization improve, but the time required for interface generation increases
Solution Approach 1:
Content is pre-aggregated and pre-rated by specialized engines (ad engine, media program campaign engine, recommendation engine) before being passed to the video delivery service. These engines prepare content with relevance scores in advance, so when the user interface needs to be generated, the selection process can proceed quickly using pre-computed ratings rather than evaluating content from scratch.
Solution Approach 2:
The system maintains continuous operation of multiple content engines that constantly generate and update content ratings. Instead of batch-processing content periodically, the ad engine, media program campaign engine, and recommendation engine operate continuously, ensuring that relevant content and scores are always available when needed for interface generation, minimizing delays.
3Quantity of substance
If the system displays more content in the user interface, then the quantity of information provided increases, but the user interface becomes less focused and may reduce user engagement
Solution Approach 1:
Different portions of the user interface are assigned different qualities and purposes. The video delivery service reserves specific portions of the interface for display content while leaving other portions for user-selected videos and interactions. Each region is optimized for its specific function, creating a balanced interface that provides sufficient content without overwhelming the user.
Solution Approach 2:
The system dynamically changes parameters such as the number of content items displayed, their positioning, and their prominence based on user context and engagement metrics. By adjusting these parameters, the system can optimize the balance between providing sufficient content quantity and maintaining interface effectiveness, ensuring that displayed content is neither too sparse nor too overwhelming.
Data Source
AI summary
In some embodiments, a method generates combinations of ad campaign content and media program campaign content and ranks the combinations of ad campaign content and media program content. The ranking is based on ad campaign content being shown with the media program campaign content. The method selects one or more of the combinations of ad campaign content and media program campaign content based on the ranking. Then, display of the one or more of the combinations of ad campaign content and media program campaign content is caused where the one or more of the combinations of ad campaign content and media program campaign content include ad campaign content shown with media program campaign content in a same area of a display.


