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数据分析与知识发现  2021, Vol. 5 Issue (2): 94-105     https://doi.org/10.11925/infotech.2096-3467.2020.0521
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于多源信息融合的音乐推荐方法 *
李丹阳,甘明鑫()
北京科技大学经济管理学院 北京 100083
Music Recommendation Method Based on Multi-Source Information Fusion
Li Danyang,Gan Mingxin()
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
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摘要 

【目的】 利用多源信息融合构建音乐特征体系,解决音乐推荐冷启动问题,为用户提供个性化音乐推荐。【方法】 采用基于多源信息融合的两段式推荐模型。通过神经网络融合多源信息,构建音乐特征体系,预测音乐的潜在因子向量,从而解决音乐冷启动问题,实现TopN推荐。【结果】 在百万歌曲数据集上开展实验,所提出的方法与CNN模型相比,在F1值上的提升幅度达到9.13%,在RMSE、MAE上的降低幅度分别达到8.08%和3.91%。【局限】 两段式推荐方法较端到端的训练有更大的局限性;此外,使用梅尔频谱训练占用内存资源较高。【结论】 所提方法构建音乐特征体系,解决了音乐推荐冷启动问题,提高了音乐推荐性能。

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李丹阳
甘明鑫
关键词 音乐推荐深度学习信息融合神经网络    
Abstract

[Objective] This paper creates a musical feature system based on multi-source information, aiming to address the cold start issue facing music recommendation and provide personalized services. [Methods] We proposed a two-stage model with multi-source information fused by neural network algorithm. Then, we built the musical feature system and predicted the potential factor vectors of music. Finally, we generated the TopN recommendation list for the users. [Results] We examined our model with the Million Song Dataset. Compared with other models such as CNN, the F1 value was improved by 9.13%, and the RMSE, MAE values were reduced by 8.08% and 3.91%, respectively. [Limitations] Our new method encounters more limits than the end-to-end training ones. And training with the Mel-frequency spectrum demands much more memory. [Conclusions] The proposed model improves the performance of music recommendation services.

Key wordsMusic Recommendation    Deep Learning    Information Fusion    Neural Network
收稿日期: 2020-06-04      出版日期: 2021-03-11
ZTFLH:  G350  
基金资助:*国家自然科学基金项目(71871019);*国家自然科学基金项目(71471016);*国家自然科学基金项目(71531013)
通讯作者: 甘明鑫 ORCID:0000-0001-8751-0780     E-mail: ganmx@ustb.edu.cn
引用本文:   
李丹阳, 甘明鑫. 基于多源信息融合的音乐推荐方法 *[J]. 数据分析与知识发现, 2021, 5(2): 94-105.
Li Danyang, Gan Mingxin. Music Recommendation Method Based on Multi-Source Information Fusion. Data Analysis and Knowledge Discovery, 2021, 5(2): 94-105.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0521      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I2/94
Fig.1  音乐推荐方法总体框架
Fig.2  神经网络模型结构
Fig.3  音频数据处理流程
Fig.4  LSTM单元结构
Track_id Title Song_id Release
TRACCVZ128F4291A8A Deep Sea Creature SOVLGJY12A8C13FBED Call of the Mastodon
TRACCMH128F428E4CD No Quieras Marcharte SOGDQZK12A8C13F37C Adelante
TRACCJA128F149A144 Segredo SODPNJR12A6D4FA52D Joao Voz E Violato
TRACCUS128F92E1FEB Bedroom Acoustics SOMMSMW12A8C13FCCC Plug In Baby
TRACJDX12903CD4917 Church Hangover SOSJVGP12AB0185B95 El Mas Chingon
Artist_id Artist_mbid Artist_name Year
ARMQHX71187B9890D3 bc5e2ad6-0a4a-4d90-b911-e9a7e6861727 Mastodon 2001
AR2PT4M1187FB55B1A d54ea4a6-0b9c-4e47-bed0-289ae9ff4037 Los Chichos 1984
AR3THYK1187B999F1F 286ec4c2-b5ca-4f85-b331-280a6d73dd14 Jo?o Gilberto 2000
ARR3ONV1187B9A2F59 fd857293-5ab8-40de-b29e-55a69d4e4d0f Muse 0
ARYF20K1187B9B76BD 4a41c200-153b-4158-87b3-db0fea627e2c George Lopez 2006
Table 1  音乐元数据信息表
User_id Song_id Play_num
d3f65887d5eb4eac8ca14d0f3085c5021e79a8c5 SOAWFKO129F06933A7 2
4fd90e488869fffe6e2186a489311bdb19ebce46 SOUGUKH12A8C13FBB0 22
8e62f55bc5830dfcbe92339afd86fda1d7fccb5e SOOQUVZ12A6D4F7536 3
747155a884e6c96299a1a052e89ead19f31ad235 SONDICG12AB0184D30 6
3446fc0b811c4970eafe30866f873e44567d371c SOODZFJ12A6D4F8D03 1
Table 2  用户-音乐收听记录表
Artist_id Tag
AR002UA1187B9A637D garage rock
AR002UA1187B9A637D free jazz
AR002UA1187B9A637D pop
AR003FB1187B994355 rock
AR003FB1187B994355 hip hop
Table 3  歌手标签表
模型 MSE RMSE MAE MSLE
CNN 1.0 156 0.8 734 0.4 580 0.1 099
Year 1.0 129 0.8 727 0.4 599 0.1 098
CNN+LSTM 0.9 668 0.9 450 0.4 833 0.1 073
Category 0.9 648 0.8 580 0.4 548 0.1 035
Artist 0.8 821 0.8 232 0.4 457 0.0 921
本文方法 0.7 942 0.8 028 0.4 401 0.0 894
Table 4  误差对比
Fig.5  准确率结果
Fig.6  召回率结果
Fig.7  F1值结果
模型 准确率 召回率 F1
N=3 N=4 N=3 N=4 N=3 N=4
CNN+LSTM 0.6 075 0.5 543 0.7 160 0.8 229 0.5 992 0.6 065
CNN 0.6 104 0.5 552 0.7 237 0.8 307 0.6 038 0.6 094
Year 0.6 269 0.5 612 0.7 517 0.8 480 0.6 230 0.6 176
Category 0.6 445 0.5 739 0.7 746 0.8 661 0.6 426 0.6 318
Artist 0.6 595 0.5 867 0.7 724 0.8 665 0.6 491 0.6 397
本文方法 0.6 682 0.5 888 0.7 843 0.8 711 0.6 589 0.6 431
Table 5  推荐评价指标对比
Fig.8  音乐三维聚类图
Fig.9  潜在因子向量可视化结果
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