Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2016

Abstract

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.

Keywords

personalized, semantic music retrieval, topic model

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

SIGIR '16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 17-21, Pisa, Italy

First Page

125

Last Page

134

ISBN

9781450340694

Identifier

10.1145/2911451.2911491

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2911451.2911491

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