Research on Personalized Music Recommendation Model Based on Personal Emotion and Collaborative Filtering Algorithm

Wang Yingqiang, A.Serrano Elcid
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:404-412, 2024.

Abstract

In today’s digital age, music plays an important role in people’s lives, but the current music recommendation system is mainly based on content, the use of collaborative filtering and other algorithms, can not according to the user’s real-time emotional state, recommend suitable for the current mood of the music. This paper aims to design and implement a personalized music recommendation model based on personal emotional information and collaborative filtering algorithm. The model mainly includes two sub-models: the emotion and music selection tendency model and the music recommendation model based on collaborative filtering algorithm. By analyzing the user’s emotional state and music preference, the model provides music recommendation services that are more in line with the user’s psychological state, so as to improve the user experience and recommendation accuracy. The model designed in this paper can effectively solve the problem that personalized music recommendation cannot be performed according to the user’s real-time emotion. At the same time, it can also be used as a reference for other personalized recommendation models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-yingqiang24a, title = {Research on Personalized Music Recommendation Model Based on Personal Emotion and Collaborative Filtering Algorithm}, author = {Yingqiang, Wang and Elcid, A.Serrano}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {404--412}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/yingqiang24a/yingqiang24a.pdf}, url = {https://proceedings.mlr.press/v245/yingqiang24a.html}, abstract = {In today’s digital age, music plays an important role in people’s lives, but the current music recommendation system is mainly based on content, the use of collaborative filtering and other algorithms, can not according to the user’s real-time emotional state, recommend suitable for the current mood of the music. This paper aims to design and implement a personalized music recommendation model based on personal emotional information and collaborative filtering algorithm. The model mainly includes two sub-models: the emotion and music selection tendency model and the music recommendation model based on collaborative filtering algorithm. By analyzing the user’s emotional state and music preference, the model provides music recommendation services that are more in line with the user’s psychological state, so as to improve the user experience and recommendation accuracy. The model designed in this paper can effectively solve the problem that personalized music recommendation cannot be performed according to the user’s real-time emotion. At the same time, it can also be used as a reference for other personalized recommendation models.} }
Endnote
%0 Conference Paper %T Research on Personalized Music Recommendation Model Based on Personal Emotion and Collaborative Filtering Algorithm %A Wang Yingqiang %A A.Serrano Elcid %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-yingqiang24a %I PMLR %P 404--412 %U https://proceedings.mlr.press/v245/yingqiang24a.html %V 245 %X In today’s digital age, music plays an important role in people’s lives, but the current music recommendation system is mainly based on content, the use of collaborative filtering and other algorithms, can not according to the user’s real-time emotional state, recommend suitable for the current mood of the music. This paper aims to design and implement a personalized music recommendation model based on personal emotional information and collaborative filtering algorithm. The model mainly includes two sub-models: the emotion and music selection tendency model and the music recommendation model based on collaborative filtering algorithm. By analyzing the user’s emotional state and music preference, the model provides music recommendation services that are more in line with the user’s psychological state, so as to improve the user experience and recommendation accuracy. The model designed in this paper can effectively solve the problem that personalized music recommendation cannot be performed according to the user’s real-time emotion. At the same time, it can also be used as a reference for other personalized recommendation models.
APA
Yingqiang, W. & Elcid, A.. (2024). Research on Personalized Music Recommendation Model Based on Personal Emotion and Collaborative Filtering Algorithm. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:404-412 Available from https://proceedings.mlr.press/v245/yingqiang24a.html.

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