Building a content-based music recommender-system [Amol Mavuduru @TowardsDataScience]
Amol Mavuduru wrote a tutorial on building a content-based music recommender system with Spotify data.
Have you ever wondered how Spotify recommends songs and playlists based on your listening history? Do you wonder how Spotify manages to find songs that sound similar to the ones you’ve already listened to?
Interestingly, Spotify has a web API that developers can use to retrieve audio features and metadata about songs such as the song’s popularity, tempo, loudness, key, and the year in which it was released. We can use this data to build music recommendation systems that recommend songs to users based on both the audio features and the metadata of the songs that they have listened to.
In this article, I will demonstrate how I used a Spotify song dataset and Spotipy, a Python client for Spotify, to build a content-based music recommendation system.
https://towardsdatascience.com/how-to-build-an-amazing-music-recommendation-system-4cce2719a572
Techtiefen: Recommender Systems (German Podcast by Heise)
Scalable Recommender Systems with NVTabular- A Fast Tabular Data Loading and Transformation Library [Ronay Ak @ Medium]
A Case For Embeddings in Recommendation Problems [Arnab Bhadury]
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).