Exploring User-Specific Information in Music Retrieval

Zhiyong Cheng, Jialie Shen, Liqiang Nie, Tat Seng Chua, Mohan Kankanhalli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

With the advancement of mobile computing technology and cloud-based streaming music service, user-centered music retrieval has become increasingly important. User-specific information has a fundamental impact on personal music preferences and interests. However, existing research pays little attention to the modeling and integration of user-specific information in music retrieval algorithms/models to facilitate music search. In this paper, we propose a novel model, named User-Information-Aware Music Interest Topic (UIAMIT) model. The model is able to effectively capture the influence of user-specific information on music preferences, and further associate users' music preferences and search terms under the same latent space. Based on this model, a user information aware retrieval system is developed, which can search and re-rank the results based on age-And/or gender-specific music preferences. A comprehensive experimental study demonstrates that our methods can significantly improve the search accuracy over existing text-based music retrieval methods.

Original languageEnglish
Title of host publicationSIGIR 2017: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages655-664
Number of pages10
ISBN (Electronic)9781450350228
DOIs
Publication statusPublished - 07 Aug 2017
Externally publishedYes
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: 07 Aug 201711 Aug 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
CountryJapan
CityTokyo, Shinjuku
Period07/08/201711/08/2017

Keywords

  • Reranking
  • Semantic Music Retrieval
  • Topic Model
  • User Demographic Information

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

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