On effective personalized music retrieval via exploring online user behaviors

In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer Music Preference Topic Model (DL-MPTM) is proposed to c...

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Main Authors: CHENG, Zhiyong, SHEN, Jialie, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3417
https://ink.library.smu.edu.sg/context/sis_research/article/4418/viewcontent/Oneffectivepersonalizedmusicretrievalviaexploringonlineuserbehaviors.pdf
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spelling sg-smu-ink.sis_research-44182018-11-30T08:47:59Z On effective personalized music retrieval via exploring online user behaviors CHENG, Zhiyong SHEN, Jialie HOI, Steven C. H. In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system's performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results demonstrate that our proposed method significantly outperforms those approaches in terms of personalized search accuracy. 2016-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3417 info:doi/10.1145/2911451.2911491 https://ink.library.smu.edu.sg/context/sis_research/article/4418/viewcontent/Oneffectivepersonalizedmusicretrievalviaexploringonlineuserbehaviors.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University personalized semantic music retrieval topic model Computer Sciences Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic personalized
semantic music retrieval
topic model
Computer Sciences
Databases and Information Systems
spellingShingle personalized
semantic music retrieval
topic model
Computer Sciences
Databases and Information Systems
CHENG, Zhiyong
SHEN, Jialie
HOI, Steven C. H.
On effective personalized music retrieval via exploring online user behaviors
description In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system's performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results demonstrate that our proposed method significantly outperforms those approaches in terms of personalized search accuracy.
format text
author CHENG, Zhiyong
SHEN, Jialie
HOI, Steven C. H.
author_facet CHENG, Zhiyong
SHEN, Jialie
HOI, Steven C. H.
author_sort CHENG, Zhiyong
title On effective personalized music retrieval via exploring online user behaviors
title_short On effective personalized music retrieval via exploring online user behaviors
title_full On effective personalized music retrieval via exploring online user behaviors
title_fullStr On effective personalized music retrieval via exploring online user behaviors
title_full_unstemmed On effective personalized music retrieval via exploring online user behaviors
title_sort on effective personalized music retrieval via exploring online user behaviors
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3417
https://ink.library.smu.edu.sg/context/sis_research/article/4418/viewcontent/Oneffectivepersonalizedmusicretrievalviaexploringonlineuserbehaviors.pdf
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