Video Ranking System Using Multi-Factor Scoring
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Solution Overview
Problem
Existing video identification systems rely heavily on popularity metrics, which can exclude high-quality videos created by users with limited followings, leading to inefficient user experiences as users must navigate through numerous screens to find relevant content.
Innovation Solution
A top video identification platform that considers interactions by both creators and consumers, including normalized popularity and other engagement metrics, to rank and curate videos, providing a more comprehensive and efficient way to present top videos to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video ranking is based on popularity metrics, then widely viewed videos are prioritized, but high-quality videos from users with limited followings are excluded
Solution Approach 1:
The patent changes the ranking parameters from simple popularity metrics to a multi-factor scoring system that includes engagement quality, creator expertise, content relevance, and interaction patterns. This allows videos to be evaluated on multiple dimensions rather than a single popularity metric, enabling high-quality videos from lesser-known creators to be discovered and ranked appropriately.
Solution Approach 2:
The patent creates a composite ranking score that combines multiple different factors (engagement metrics, creator credentials, content characteristics, user interactions) into a unified evaluation system. This composite approach allows the system to capture both popularity and quality aspects simultaneously, resolving the contradiction between favoring established creators and discovering new talent.
2Quantity of substance
If users navigate through numerous screens to find relevant content, then comprehensive content is available, but user experience becomes inefficient
Solution Approach 1:
The patent performs preliminary ranking and curation of videos based on the multi-factor scoring system before users search. By pre-organizing content according to quality and relevance metrics, the system presents users with a curated list of top videos that are most likely to be relevant and high-quality, eliminating the need for users to manually navigate through numerous screens to discover good content.
Solution Approach 2:
The patent incorporates user interaction feedback (views, likes, shares, watch time) into the ranking system, allowing the content recommendations to adapt and improve based on actual user behavior. This feedback loop enables the system to learn what content users find valuable and automatically prioritize similar high-quality videos in future recommendations, reducing search time while maintaining content availability.
Data Source
AI summary
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for ranking videos. The program and method include storing, in a database, a plurality of videos; obtaining, for a first of the plurality of videos, first data representing a first interaction performed by a creator of the first video in association with the first video; and obtaining, for the first video, second data representing a second interaction performed by a consumer of the first video in association with the first video. A score is computed for the first video based on the first and second data and the first video is ranked relative to a remaining subset of the plurality of videos by comparing the computed score for the first video with respective scores of videos in the remaining subset of the plurality of videos.