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The `MKGCN` class, coupled with the Spotify API, orchestrates a multi-modal knowledge graph convolutional network to enhance music recommendation systems by integrating user interaction data and diverse music modalities.
This project summarizes the basic steps required to implement a basic recommendation engines that suggests new bands to users. Data are fetched from the open dataset of ListenBrainz in Bigquery. The recommendation engine is built by hacking the keras embedding layers to perform matrix factorization.
This is a simple song recommendation system which uses concepts of Machine Learning to provide personalized experience based on the past history of the user and user's choice preferences.
The goal is to build a music recommendation system that can provide custom playlists for individual users based on collaborative and metadata filtering.
Client-side application for Vibify, a JavaScript-based Spotify API interaction tool. Features include fetching user's top tracks, creating playlists, getting audio features for a playlist, and a recommendation system.