基于多源信息融合的音乐推荐方法  

Music Recommendation Method Based on Multi-Source Information Fusion

作　　者：李丹阳 甘明鑫[1] Li Danyang;Gan Mingxin(School of Economics and Management,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学经济管理学院,北京100083

出　　处：《数据分析与知识发现》2021年第2期94-105,共12页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:71871019,71471016,71531013)的研究成果之一。

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F_(1)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。[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 Top N recommendation list for the users.[Results]We examined our model with the Million Song Dataset.Compared with other models such as CNN,the F_(1)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.

关 键 词：音乐推荐 深度学习 信息融合 神经网络 

分 类 号：G350[文化科学—情报学]