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Recommending More Suitable Music Based on Users’ Real Context

  • Qing Yang
  • Le Zhan
  • Li Han
  • Jingwei ZhangEmail author
  • Zhongqin Bi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method.

Keywords

Music recommendation Feature selection User context 

Notes

Acknowledgments

This study is funded by the National Natural Science Foundation of China (No. 61462017, 61862013, U1501252, U1711263, 61662015), Guangxi Natural Science Foundation of China (No. 2017GXNSFAA198035), and Guangxi Cooperative Innovation Center of Cloud Computing and Big Data.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Qing Yang
    • 1
  • Le Zhan
    • 1
  • Li Han
    • 1
  • Jingwei Zhang
    • 2
    • 3
    Email author
  • Zhongqin Bi
    • 4
  1. 1.School of Electronic Engineering and AutomationGuilin University of Electronic TechnologyGuilinChina
  2. 2.Guangxi Cooperative Innovation Center of Cloud Computing and Big DataGuilin University of Electronic TechnologyGuilinChina
  3. 3.Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilinChina
  4. 4.College of Computer Science and TechnologyShanghai University of Electronic PowerShanghaiChina

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