Towards a better understanding of music playlist titles and descriptions

Music playlists, either user-generated or curated by music streaming services, often come with titles and descriptions. Although informative, these titles and descriptions make up a sparse and noisy semantic space that is challenging to be leveraged for tasks such as making music recommendations. This dissertation is dedicated to developing a better understanding of playlist titles and descriptions by leveraging track sequences in playlists. Specifically, work has been done to capture latent patterns in tracks by an embedding approach, and the latent patterns are found to be well aligned with the organizing principles of mixtapes identified more than a decade ago. The effectiveness of the latent patterns is evaluated by the task of generating descriptive keywords/tags for playlists given tracks, indicating that the latent patterns learned from tracks in playlists are able to provide a good understanding of playlist titles and descriptions. The identified latent patterns are further leveraged to improve model performance on the task of predicting missing tracks given playlist titles and descriptions. Experimental results show that the proposed models yield improvements to the task, especially when playlist descriptions are provided as model input in addition to titles. The main contributions of this work include (1) providing a better solution to dealing with ``cold-start'' playlists in music recommender systems, and (2) proposing an effective approach to automatically generating descriptive keywords/tags for playlists using track sequences.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Information Sciences

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Hao, Yun

Contributors dc:contributor

• Downie, J. Stephen
• Hu, Xiao
• Torvik, Vetle
• Ehmann, Andreas
• Bosch, Nigel

• natural language processing
• recommender systems

Statement dc:rights

• Copyright 2021 Yun Hao

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110509

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110509