Abstract
This paper proposes a new method to control embedded representation of a piece of music provided as a set of playlists. To recommend an appropriate piece to a user, numerical representation of music has been studied so far. This study does not focus on explicit representation like signal data, rather implicit representation called embeddings obtained from users’ playlists in order to avoid issues around copyright. In the previous work, naive method was proposed and raw dataset is provided to learn the model of embedding for pieces of music, however, it is still not clear the raw dataset is appropriate for the model of music recommender system. Actually, this paper shows there is bias in a raw dataset and it makes the representation tends to provide trivial result, i.e., clearly same element that can be implied only by shallow knowledge like that pieces of music composed by the same artist are similar each other. This study shows the problem quantitatively and proposes a new method to reduce self-evident feature from embedded representation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Harris, Z.S.: Distributional Structure. WORD 10(2–3), 146–162 (1954)
Mikolov, T., Chen, K., Corrad, o G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations Workshops Track (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural. Inf. Process. Syst. 26, 3111–3119 (2013)
Krishnamurthy, B., Puri, N., Goel, R.: Learning vector-space representations of items for recommendations using word embedding models. Procedia Comput. Sci. 80, 2205–2210 (2016)
Yoon, Y., Lee, J.: Movie recommendation using metadata based Word2Vec algorithm. In: 2018 International Conference on Platform Technology and Service, pp. 1–6 (2018)
Amiri, M., Shobi, A.: A link prediction strategy for personalized tweet recommendation through Doc2Vec approach. Res. Econ. Manag. 2(4), 63–76 (2017)
Baltrunas, L., et al.: InCarMusic: context-aware music recommendations in a car. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 89–100. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23014-1_8
Yapriady, B., Uitdenbogerd A.: Combining demographic data with collaborative filtering for automatic music recommendation. In: 9th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 201–207 (2005)
Radhika, N.: Music recommendation system based on user’s sentiment. Int. J. Sci. Res. 383–384 (2015)
Jawaheer, G., Szomszor, M., Kostkova, P.: Comparison of implicit and explicit feedback from an online music recommendation service. In: International Workshop on Information Heterogeneity and Fusion in Recommender Systems (2010)
Bogdanov, D., Haro, M., Fuhrmann, F., Gómez, E., Herrera, P.: Content-based music recommendation based on user preference examples. In: Workshop on Music Recommendation and Discovery, Colocated with ACM RecSys 2010 (2010)
Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: Proceedings of the 13th ACM International Conference on Multimedia (2005)
Wang, D., Deng, S., Xu, G.: Sequence-based context-aware music recommendation. Inf. Retrieval J. 21, 230–252 (2018)
Zhou, Y., Tian, P.: Context-aware music recommendation based on Word2Vec. In: International Computer Science and Applications Conference, pp. 50–54 (2019)
Köse, B., Eken, S., Sayar, A.: Playlist generation via vector representation of songs. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds.) INNS 2016. AISC, vol. 529, pp. 179–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47898-2_19
Baccigalupo, C., Plaza, E.: Case-Based Sequential Ordering of Songs for Playlist Recommendation, pp. 286–300. Advances in Case-Based Reasoning, European conference (2006)
Flexer, A., Schnitzer, D., Gasser, M., Widmer, G.: Playlist generation using start and end songs. In: International Conference on Music Information Retrieval, pp. 173–178 (2008)
Pichl, M., Zangerle, E., Specht, G.: Towards a context-aware music recommendation approach: what is hidden in the playlist name?. In: Proceedings of 15th IEEE International Conference on Data Mining Workshops, pp. 1360–1365 (2015)
Acknowledgements
This work was partially supported by JSPS KAKENHI Grant Numbers 21H03553, 22H03698, and 22K19836.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shibata, H., Ebine, K., Takama, Y. (2024). Method to Control Embedded Representation of Piece of Music in Playlists. In: **n, B., Kubota, N., Chen, K., Dong, F. (eds) Advanced Computational Intelligence and Intelligent Informatics. IWACIII 2023. Communications in Computer and Information Science, vol 1931. Springer, Singapore. https://doi.org/10.1007/978-981-99-7590-7_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-7590-7_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7589-1
Online ISBN: 978-981-99-7590-7
eBook Packages: Computer ScienceComputer Science (R0)