Diseases Video Recommender System using Keyword-Based Vector Space on Youtube and Vimeo

Abstract

Digital health solutions can be done in various ways, one of which is by searching for information on the internet. However, when someone searches on a search engine, the videos that are displayed are only videos based on keywords, without considering what kind of videos the user likes. Meanwhile, when searching for videos on YouTube, the recommended videos are only videos found on YouTube, so the range of recommended videos is limited. To overcome this problem, we build a web-based video recommender system about diseases that is more organized with a wider range of videos taken from YouTube and Vimeo. In addition, the system not only recommends videos based on the searched keywords but also recommends videos based on videos that are liked by users. The YouTube and Vimeo APIs are used to retrieve videos about the disease being searched for. We use content-based filtering for the recommendation process. Keyword-based vector space does some tasks: 1) converts the title and description of a video into a vector space, 2) calculates the cross product of the term frequency, 3) determines the proximity of the title using cosine similarity. The test results show that the average performance is 92.67% according to the purpose of the recommendation system made, namely novelty and relevance.