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Representation, Exploration, and Recommendation of Music Playlists


Abstract Playlists have become a significant part of the music listening experience today because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise in usage of playlists, recommending playlists is crucial to music services today. Although there has been a lot of work done in playlist prediction, the area of playlist representation hasn't received that level of attention. Over the last few years, sequence-to-sequence models, especially in the field of natural language processing have shown the effectiveness of learned embeddings in capturing the semantic characteristics of sequences. Similar concepts can be applied to music to learn fixed length representations for playlists and the learned repr... (more)
Created Date 2019
Contributor Papreja, Piyush (Author) / Panchanathan, Sethuraman (Advisor) / Demakethepalli Venkateswara, Hemanth Kumar (Committee member) / Amor, Heni Ben (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Computer science / Embedding / Information Retrieval / Music Playlists / Recommendation / Sequence-2-sequence
Type Masters Thesis
Extent 62 pages
Language English
Copyright
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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