# name
Word2vec embeddings for playlist recommendation

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Santi Seguí Mesquida i Guillem Pascual i Guinovart

# metadata.dc.description.abstract[].value
[en] We present an ML approach to musical playlist recommendation. Using the algorithm Word2Vec, a shallow two-layer neural network trained to reconstruct linguistic context of words, we have created several embeddings using tracks and playlist titles as words of an artificial vocabulary. Some experiments with different trade-offs between the diversity and the popularity of songs in playlists are analyzed and discussed. By means of combining a tracks embedding and a titles embedding our recommender has reached 19 percent of accuracy. Our model has been created and trained using the MPD (million playlists dataset) given by Spotify as part of the RecSys Challenge 2018.

# metadata.dc.rights[].value
memòria: cc-by-nc-sa (c) Anna Bach Valls, 2018

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

# metadata.dc.title[].value
Word2vec embeddings for playlist recommendation