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Additionally, recommendation algorithms based on personal history can suffer from the bubble effect.
The bubble effect is when only content relevant to your history appears in the recommendation algorithm, exposing users to biased content.
Recently, crimes have been committed to exploit this to expose children to harmful videos, raising concerns about recommendation algorithms and video censorship schemes.
To solve these problems, it is necessary to strengthen the censorship work and introduce stricter regulations to block and remove harmful content, which requires a lot of time and manpower.
In this study, we use AI-based content analysis to extract contextual data from real-world content.
By indexing the extracted contextual data into videos and clips, we propose a model to extend metadata and improve search and recommendation systems.
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IEEE/ICACT20240357 Slide.07
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