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Title:Towards a better understanding of music playlist titles and descriptions
Author(s):Hao, Yun
Director of Research:Downie, J. Stephen
Doctoral Committee Chair(s):Downie, J. Stephen
Doctoral Committee Member(s):Hu, Xiao; Torvik, Vetle; Ehmann, Andreas; Bosch, Nigel
Department / Program:Information Sciences
Discipline:Information Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):natural language processing
recommender systems
Abstract: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.
Issue Date:2021-04-20
Type:Thesis
URI:http://hdl.handle.net/2142/110509
Rights Information:Copyright 2021 Yun Hao
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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