Video Distribution With Dictionary-Validated Preference Tags
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Solution Overview
Problem
Viewers often face a time-consuming process in finding video content that aligns with their personal preferences, leading to a lack of active communication with distributors, despite the potential for enhanced engagement through shared interests.
Innovation Solution
A video distribution device that acquires user identification and favorite tag information, associates tags with external dictionary data to suppress mismatches, and generates video data for distribution based on shared preferences, incorporating real-time comments and emote images to facilitate communication between viewers and distributors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If viewers manually search for video content from an enormous amount of distributed content, then they can find content that interests them, but the process is time-consuming and reduces active communication
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing recommendation values for each viewer based on their profile and video attributes before the viewer actually searches. The recommendation engine pre-processes matching between viewer preferences and video content, so when a viewer accesses the system, pre-computed recommendations are immediately available, eliminating the need for time-consuming manual searching.
Solution Approach 2:
The system enables self-service by automatically generating personalized video recommendations for each viewer based on their profile data and the attributes of distributed videos. The recommendation engine autonomously matches viewers with suitable content without requiring manual intervention, and the system self-adjusts recommendations based on viewer feedback and behavior patterns.
2Productivity
If viewers find video content independently without preference matching, then they can access content, but active communication with distributors does not occur
Solution Approach 1:
The system implements feedback mechanisms where viewer interactions with recommended content (viewing behavior, comments, likes) are continuously monitored and fed back into the recommendation engine. This feedback loop allows the system to learn from viewer preferences and adjust recommendations accordingly, while also providing distributors with information about viewer engagement patterns, thereby fostering active communication and preference alignment.
Solution Approach 2:
The recommendation system acts as an intermediary that bridges viewers and distributors by matching preferences before content delivery. The system mediates the interaction by presenting pre-matched content that aligns with viewer preferences, and by providing distributors with aggregated viewer preference data, enabling informed communication and content creation.
3Ease of manufacture
If the system stores raw favorite tag candidate information without validation, then registration is simple, but mismatching of favorite tags between distributors and viewers occurs
Solution Approach 1:
The dictionary data serves as an intermediary layer between raw user input and the final tag storage. When a viewer or distributor registers a favorite tag, the system checks it against the dictionary data to ensure consistency and standardization. This intermediary validation process maintains ease of registration while ensuring reliability of tag matching by preventing mismatches caused by variations in tag naming.
Data Source
AI summary
A video distribution device configured to provide a service enabling communication between distributors that distribute video content and viewers thereof and that increases excitement in the video content including: a first acquiring part configured to acquire real-time video data supplied by a distributor terminal and the user identification information of a distributor; a second acquiring part configured to acquire a distribution request from a viewer terminal and the user identification information of the viewer; a third acquiring part configured to acquire user identification information from a user terminal, possibly including the distributor terminal and the viewer terminal and candidate information of a favorite tag for classifying distributors or viewers by interests or favorited items; and a user information managing part configured to associate the favorite tag candidate information with the user identification information acquired by the third acquiring part as the favorite tag with reference to the external dictionary data when the favorite tag candidate information is included in the dictionary data and storing this in a database.