Learning Playlist Representations for Automatic Playlist Generation
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Lärande av spellisterepresentationer för automatisk spellistegenerering (Swedish)
Spotify is currently the worlds leading music streaming ser-vice. As the leader in music streaming the task of providing listeners with music recommendations is vital for Spotify. Listening to playlists is a popular way of consuming music, but traditional recommender systems tend to fo-cus on suggesting songs, albums or artists rather than pro-viding consumers with playlists generated for their needs.
This thesis presents a scalable and generalizeable approach to music recommendation that performs song selection for the problem of playlist generation. The approach selects tracks related to a playlist theme by finding the charac-terizing variance for a seed playlist and projects candidate songs into the corresponding subspace. Quantitative re-sults shows that the model outperforms a baseline which is taking the full variance into account. By qualitative results the model is also shown to outperform professionally curated playlists in some cases.
Place, publisher, year, edition, pages
Playlist generation, machine learning, music recommendation
IdentifiersURN: urn:nbn:se:kth:diva-172845OAI: oai:DiVA.org:kth-172845DiVA: diva2:850112
Master of Science - Computer Science
Ek, Carl Henrik