Improving music recommendation using distributed representation

Dongjing WANG; Shuiguang DENG; Songguo LIU; Guandong XU

doi:10.1145/2872518.2889399

Improving music recommendation using distributed representation

Dongjing WANG
, Shuiguang DENG
, Songguo LIU
, Guandong XU

Offices of the President (P)

Research output: Chapter in Book/Report/Conference proceeding › Chapters

15 Citations (Scopus)

Abstract

In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods. Copyright © 2016 owner/author(s).

Original language	English
Title of host publication	Proceedings of the 25th International Conference on World Wide Web
Place of Publication	Switzerland
Publisher	International World Wide Web Conferences Steering Committee
Pages	125-126
ISBN (Electronic)	9781450341448
DOIs	https://doi.org/10.1145/2872518.2889399
Publication status	Published - Apr 2016

Keywords

Music recommendation
Distributed representation

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10.1145/2872518.2889399

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abstract = "In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods. Copyright {\textcopyright} 2016 owner/author(s).",

author = "Dongjing WANG and Shuiguang DENG and Songguo LIU and Guandong XU",

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Improving music recommendation using distributed representation

Abstract

Keywords

Access to Document

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