Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/130481
Title: Word2vec embeddings for playlist recommendation
Author: Bach Valls, Anna
Director/Tutor: Seguí Mesquida, Santi
Pascual i Guinovart, Guillem
Keywords: Sistemes d'ajuda a la decisió
Xarxes neuronals (Informàtica)
Programari
Treballs de fi de grau
Música
Decision support systems
Neural networks
Computer software
Music
Bachelor's thesis
Issue Date: Jun-2018
Abstract: [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.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Santi Seguí Mesquida i Guillem Pascual i Guinovart
URI: http://hdl.handle.net/2445/130481
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

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