Content based Video Recommendation System

Harshal Shrimali, Rahul Saxena, Kavita
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Abstract

We launched a new method of recommending youTube videos. Our algorithm suggests videos based on their semantic similarity to the query using the transcripts of the videos and ranking them using natural language ranking algorithms. In terms of quality and information, our method has the potential to provide better-related videos than tag-based recommendation. This can be useful for recommending educational videos and can be used on ed-tech platforms and MOOC course platforms.
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基于内容的视频推荐系统
我们推出了一种推荐youTube视频的新方法。我们的算法根据视频文本与查询的语义相似度来推荐视频,并使用自然语言排序算法对其进行排序。在质量和信息方面,我们的方法有可能提供比基于标签的推荐更好的相关视频。这对推荐教育视频很有用,可以在教育技术平台和MOOC课程平台上使用。
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