High level overview:

The sentence embeddings are calculated using a Bidirectional Encoder Representation Transformer (BERT) model. There's a pre-trained model for this network trained on over 1 billion sentences from the internet that is publicly available, (thanks Microsoft) . The model transforms your description into a 784-long list of numbers (a vector) that represents the meaning of your sentence.

The model runs off a dataset of musical metadata for 35,000 songs. This metadata is very rich, it has a lot of useful columns like the genres, subgenres, and descriptions of tracks. The numerical data is binned into categorical values like "obscure" mapping popularity between 0 and 10, "highly danceable" mapping danceability between 80 and 100, etc. The text data is modified into a coherent sentence: "this song's main genres are _____. this song is from the 80s. this name of this song is lovefool by the cardigans. etc"

Each feature for each song in our metadata dataset is now a big paragraph that describes the song overall. The paragraph is split up into sentences, and the embedding of each sentence is found. The final embedding for each song is then calculated by taking the mean all sentence embeddings from the big paragraph.

To make your playlist, all that has to be done is compare the embedding of your query all 35,000 embeddings in the dataset and return the 100 most similar queries, using the cosine similarity distance metric. Thank god we have computers.

Once the 100 most similar tracks are found, your playlist is made by sending the Spotify track IDs through their API, and the link is generated for you.