Content-Based Music Video Recommender System Using Cosine Similarity

A recommender system works on the basis of machine learning. The proposed work builds a music video recommender system, and it consists of two phases. First, this work constructs a network that reveals relationships between song names and artists. Second, by using textual feature extraction, link prediction is carried out by considering the song’s name prediction, which uses cosine similarity and the content of the song’s lyrics. The recommendation results for songs are based on an item matrix that uses a recommendation engine trained on a cosine similarity algorithm. The generation of recommended songs makes links to regular expressions (regex) by using a song’s name, its artist’s name, and YouTube’s application programming interface (API). It also uses web scraping to find out more about the links that are based on that request of regexes through YouTube API, the uniform resource locator (urllib) module, and a web browser module that plays the recommended songs. The effectiveness of the suggested method is assessed by calculating several variables: accuracy, precision, F1 score, and recall. The real-world “songdata” dataset has been used in this experiment to yield the work’s results.