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@article{207266,
        author = {Tanu Jaiswal and Harmandeep Kaur and Harmanpreet Kaur and Pratha Saxena},
        title = {A Data Science Approach to Personalized Music Recommendation on Spotify Using Cosine Similarity And K-Means Clustering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {177-183},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207266},
        abstract = {Today, music streaming platforms offer access to a vast number of songs, making it difficult for users to find songs that match their interests. In this project, we built a music recommendation system that can suggest songs according to user preferences. We used a dataset from Kaggle. It includes features such as danceability, energy, tempo, and valence. These features help analyse songs and better understand their characteristics. To find similar songs, cosine similarity was applied. On the other hand, K-Means clustering helped group songs. This makes it easier to understand user preferences in a better way. The combination of similarity filtering and clustering improves the recommendations and makes them more useful by providing accurate song suggestions Different graphs and visualisations are also used to understand the dataset and feature relationships. The results show that combining similarity and clustering gives better recommendations. This system can be useful in real-world music applications and improves the overall user experience. This study shows how machine learning and clustering methods can be effectively applied to develop scalable song recommendation systems.},
        keywords = {Cosine Similarity, Data Science, Data Visualisation, Feature Analysis, K-Means Clustering, Music Recommendation System, Machine Learning, Spotify Dataset},
        month = {July},
        }