🎥 Model Diagram
📖 Presentation Material
https://docs.google.com/presentation/d/1LFrR0mcRD333V0jMQCteKuijk77TETDP/edit?usp=drive_link&ouid=113654655373866451129&rtpof=true&sd=true
A recommendation program for "music". The user enters two songs, and the main class 'Music_Recommender' recommends a list of songs relative to the entered two songs.
Three filtering models were integrated to develop the music recommendation system.
Collaborative Filtering – KNN ClassifierContent Based Filtering – Lyric based Cosine SimilarityContent Based Filtering – Music Features based Sigmoid calculationDataset:
https://www.kaggle.com/datasets/maharshipandya/-spotify-tracks-datasethttp://millionsongdataset.com/Real-time crawling from Melon to obtain the latest information👬 Team Composition
Model Development (2), Presentation Material (2)
🔨 Role and Function
Collaborative Filtering, Content Based Filtering, Content Based FilteringConfiguring a big all-in-one function⚒️ Used Technologies and Libraries
Collaborative Filtering – KNN ClassifierContent Based Filtering – Lyric based Cosine SimilarityContent Based Filtering – Music Features based Sigmoid calculationCrawling - BeautifulSoup4💡 Reflections
Developing a recommendation system for my favorite field, music, by directly obtaining scattered song data from the internet was a novel experience.However, it was disappointing to find that there is not as much music data as expected.It was unfortunate that the tf-idf library supports only English.Establishing criteria for providing optimal recommendations for two songs was challenging.Integrating three models made it difficult to decide on the basis for assigning the order of results from the models.