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We applied a hybrid multi-model base system for recommending videos by analyzing the metadata of videos, users, and the content type or categories.
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We created a system that required the users to mention the skills they want to enhance (personal, interview, managerial, or leadership skills). These data points were used to build the recommendation system. Moreover, we created a repository of videos that are most watched by users based on the information gathered such as geographical location, demographic, etc.
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The system included a neural network-based filtering algorithm that utilizes users’ metadata and app usage history. This enabled the app to recommend videos to users based on the preferences of other users with similar interests and activities.
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For new videos uploaded into the system, we collected information based on the most active users who watched almost all the videos. The new videos were then recommended to these active users to gauge whether the video would be accepted by other users or would they lose interest. Based on the video content and quality information gathered, the videos were recommended to other users.
