Extraction of Meta-Data for Recommendation Using Keyword Mapping

Expanding traditional video metadata and recommendation systems encompasses challenges that are difficult to address with conventional methodologies. Limitations in utilizing diverse information when extracting video metadata, along with persistent issues like bias, cold start problems, and the filter bubble effect in recommendation systems, are primary causes of performance degradation. Therefore, a new recommendation system that integrates high-quality video metadata extraction with existing recommendation systems is necessary. This research proposes the "Extraction of Meta-Data for Recommendation using keyword mapping," which involves constructing contextualized data through object detection models and STT (Speech-to-Text) models, extracting keywords, mapping with the public dataset MovieLens, and applying a Hybrid recommendation system. The process of building contextualized data utilizes YOLO and Google's Speech-to-Text API. Following this, keywords are extracted using the TextRank algorithm and mapped to the MovieLens dataset. Finally, it is applied to a Hybrid Recommendation System. This paper validates the superiority of this approach by comparing it with the performance of the MovieLens recommendation system that does not expand metadata. Additionally, the effectiveness of metadata expansion is demonstrated through performance comparisons with existing deep learning-based keyword extraction models. Ultimately, this research resolves the cold start and long-tail problems of existing recommendation systems through the construction of video metadata and keyword extraction.