Ranking-Based Play-Video Recommendation System
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Solution Overview
Problem
Existing social network games lack a mechanism to coordinate ranking systems and play-video systems, making it difficult to motivate casual players to view rankings and create play-videos that interest them, as popular play-videos are often created by heavy players and not easily shared among casual players.
Innovation Solution
An information processing system that coordinates ranking and play-video systems by recommending suitable timings for players to create and upload play-videos based on ranking index evaluation quantities, associating play-videos with ranking information, and incentivizing players to upload videos when their rank is likely to change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If play-videos are distributed on existing video sharing websites ranked by popularity, then heavy players' videos become popular, but casual players cannot easily access videos that interest them
Solution Approach 1:
The patent introduces a ranking system as an intermediary between play-videos and players. Instead of relying on general popularity rankings, videos are associated with game ranking information, allowing casual players to find interesting content through game-specific ranking contexts rather than being overwhelmed by heavy players' popular videos
Solution Approach 2:
The patent applies local quality by creating different distribution channels for different player types. Heavy players' videos can still gain popularity through general sharing, while casual players can access relevant videos through game-specific ranking associations, allowing each group to find content suited to their needs
2Productivity
If a ranking system and play-video system are coordinated to recommend upload timings, then player motivation increases, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing ranking information and evaluation quantities before video upload. The server maintains ranking data structures and evaluation criteria in advance, so when a player uploads a video, the system can quickly associate it with appropriate ranking information without complex real-time calculations
Solution Approach 2:
The system implements feedback mechanisms where the server provides recommendation information to terminals based on ranking data, and player responses (video uploads) feed back into the ranking system. This creates a closed-loop system that automatically adjusts and improves recommendations based on actual player behavior
Data Source
AI summary
A difference calculating unit calculates the difference in evaluation quantity for a predetermined ranking index between a player of interest and another player at a higher rank than the player of interest according to the predetermined ranking index. A recording-recommendation presenting unit recommends the player of interest to create and upload a play-image for ranking information in the case where the difference has become less than or equal to a certain value.