Video Distribution Tag Matching for Faster Content Discovery
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Solution Overview
Problem
Viewers often struggle to find video content that aligns with their preferences, leading to a lack of active communication and reduced excitement in video content distribution.
Innovation Solution
A video distribution device that acquires user identification and favorite tag information, matches preferences between viewers and distributors, and generates video data for distribution, including real-time comments and emote images, to facilitate active communication and increase excitement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If viewers search for video content from an enormous amount of distributed content, then they can find content of interest, but it is a time-consuming process
Solution Approach 1:
The system performs preliminary actions by pre-calculating recommendation values for video content based on viewer profiles and video attributes before viewers search. Viewer profiles are created in advance based on viewing history and preferences, and recommendation values are computed beforehand, so when viewers access the system, they immediately receive personalized recommendations without needing to search through enormous amounts of content manually.
2Reliability
If the system provides personalized recommendations, then viewer satisfaction increases, but the complexity of the recommendation system increases
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: a profile creation unit that separates viewer profile management from recommendation generation, a video attribute extraction unit that independently processes video metadata, and a recommendation value calculation unit that combines these elements. This segmentation allows each module to be optimized independently and reduces overall system complexity while maintaining personalization accuracy.
3Measurement precision
If the system stores detailed user profile data, then recommendation accuracy improves, but data management complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from detailed user profile data for recommendation purposes. Instead of managing and processing all raw user data, the profile creation unit extracts key preference indicators such as frequently viewed content types, preferred genres, and viewing patterns. This extraction approach maintains recommendation accuracy by focusing on the most predictive features while significantly reducing data management complexity.
Data Source
AI summary
To provide a service enabling communication between distributors that distribute video content and viewers thereof and that increases excitement in the video content. A video distribution device according to an Embodiment provides: a first acquiring part for acquiring real-time video data supplied by a distributor terminal and the user identification information of a distributor; a second acquiring part for acquiring a distribution request from a viewer terminal and the user identification information of the viewer; a third acquiring part for acquiring 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 for associating 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.