A Music Recommendation Model Based on Users' Long and Short Term Preferences and Music Emotional Attention

Wu Ya-di; Chen Ping-hua

doi:10.12052/gdutxb.220009

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A Music Recommendation Model Based on Users' Long and Short Term Preferences and Music Emotional Attention

更新时间：2026-05-27

- A Music Recommendation Model Based on Users' Long and Short Term Preferences and Music Emotional Attention
- Vol. 40, Issue 4, Pages: 37-44(2023)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220009
  CLC： TP391
- Online First：25 July 2023，
  
  Published：2023
- 稿件说明：
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吴亚迪, 陈平华. 基于用户长短期偏好和音乐情感注意力的音乐推荐模型[J]. 广东工业大学学报, 2023, 40(4): 37-44.

Wu Ya-di, Chen Ping-hua. A Music Recommendation Model Based on Users' Long and Short Term Preferences and Music Emotional Attention[J]. 2023, 40(4): 37-44.
吴亚迪, 陈平华. 基于用户长短期偏好和音乐情感注意力的音乐推荐模型[J]. 广东工业大学学报, 2023, 40(4): 37-44. DOI： 10.12052/gdutxb.220009.

Wu Ya-di, Chen Ping-hua. A Music Recommendation Model Based on Users' Long and Short Term Preferences and Music Emotional Attention[J]. 2023, 40(4): 37-44. DOI： 10.12052/gdutxb.220009.

摘要

针对现有音乐推荐在用户偏好建模时忽略用户长期偏好，或对用户记录统一建模时忽略历史信息与当前情境联系的问题，提出一种基于用户长短期偏好和音乐情感注意力的音乐推荐模型。首先将用户听歌记录切分为多个历史序列和当前序列，利用多个长短期记忆网络分别进行特征提取，得到用户长短期偏好：对于历史音乐序列，提出序列时段的概念，并进行序列时段加权计算，得到长期偏好；对于当前序列，利用平均池化提取当前情景音乐特征，得到短期偏好。其次，从音乐声学信号中学习音乐情感特征，应用注意力机制计算音乐情感因子。最后，将音乐情感因子融入用户长短期偏好，得到一个音乐推荐列表。在Last.fm真实数据集上的实验结果表明，模型的NDCG@10达到了

0.5435

，优于现有方法；消融实验和特征贡献分析进一步验证了模型的有效性。

Abstract

Users' long-term preferences and the relationship between historical information and current situation are usually ignored in existing user preference modeling and user record unified modeling. To address this

in this paper

we propose a music recommendation model based on users' long-term and short-term preferences and music emotional attention. Firstly

we divide the user's listening record into multiple historical and current sequences

and learn the features by multiple long and short-term memory networks

respectively

to obtain user's long and short-term preferences. For the histo

rical music sequences

we propose the concept of sequence period is proposed to obtain the long-term preferences by calculating the weights of the sequence period. For the current sequence

we use the average pooling to extract the music features of the current scene to obtain the short-term preference. Secondly

we learn the emotional characteristics of music from the acoustic signals

and use the attention mechanism to calculate the emotional factors of music. Finally

we integrate the music emotion factor into the user's long-term and short-term preference to generate a music recommendation list. The experimental results on the last.fm real data set show that the proposed model achieves

0.5435

in term of the NDCG@10

which is better than the existing methods. The ablation experiment and characteristic contribution analysis further demonstrate the effectiveness of the model.

关键词

Keywords

references

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WANG D, DENG S, ZHANG X, et al . Learning to embed music and metadata for context-aware music recommendation [J ] . World Wide Web, 2018, 21(5): 1399-1423.

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DHAHRI C, MATSUMOTO K, HOASHI K. Mood-Aware music recommendation via adaptive song embedding[C]//2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) . Barcelona: IEEE, 2018: 135-138.

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JIN Y, HAN C. A music recommendation algorithm based on clustering and latent factor model[C]//2019 International Conference on Computer Science Communication and Network Security. Sanya: EDP Sciences, 2020: 1-9.

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