On effective personalized music retrieval by exploring online user behaviors

Zhiyong Cheng, Jialie Shen, Steven C.H. Hoi

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

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.

Original languageEnglish
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationPisa; Italy
PublisherAssociation for Computing Machinery, Inc
Pages125-134
Number of pages10
ISBN (Electronic)9781450342902
DOIs
Publication statusPublished - 7 Jul 2016
Externally publishedYes
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period17/07/1621/07/16

Cite this

Cheng, Z., Shen, J., & Hoi, S. C. H. (2016). On effective personalized music retrieval by exploring online user behaviors. In SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 125-134). Association for Computing Machinery, Inc. https://doi.org/10.1145/2911451.2911491