Music Recommendation System Using Machine Learning

Verma, Varsha; Marathe, Ninad; Sanghavi, Parth; Nitnaware, Prashant

doi:10.32628/cseit217615

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Authors Published a paper that will show us the recommended songs when a specific song is processed using libraries like NumPy and Pandas [3]. Music service providers need a useful system for categorizing recordings and helping their customers find music by providing outstanding suggestions [4].…”

Section: Related Workmentioning

confidence: 99%

Emotion-Based Music Movie and TV Series Recommendation System Using Deep Learning Algorithm

Deepa¹,

Vijayalakshmi²,

Shanmathi³

et al. 2023

ACE

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It can be challenging to decide which music or movies to listen to from a huge number of options. The major purpose of our music and movie recommendation system is to provide clients with selections that fit their tastes. An assessment of a user's facial expression may provide insight into their current emotional or mental state. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. It is feasible to assist a user in picking which music or movie to listen to or watch, by building a suggestion system. The face of the user is detected using the webcam. The snapshot of the user is taken based on their mood or feeling. It recognizes six facial expressions: angry, sad, fearful, joyful, surprised, and neutral. Based on the expression classification, the users are given three categories of recommendation as movies, music, or series based on their feelings. Seven different human facial expressions are classified using the Convolutional Neural Network (CNN) model. The Haar Cascade is an Object Detection Algorithm for recognizing faces in images and real-time video.

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Section: Related Workmentioning

confidence: 99%

Emotion-Based Music Movie and TV Series Recommendation System Using Deep Learning Algorithm

Deepa¹,

Vijayalakshmi²,

Shanmathi³

et al. 2023

ACE

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show abstract

“…Several existing systems have been created in the field of music recommendation systems to forecast song preferences based on a user's playlist, taking into consideration their likes and dislikes. One such system [4] offers a graphical user interface for users to input attribute details and forecast music preference using machine learning methods, such as decision trees, random forests, and logistic regression. The best algorithm for song prediction has been chosen after these algorithms have been tested using metrics like MAE, MSE, RMSE, and R-squared error.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches

Mukhopadhyay,

Kumar,

Parashar

et al. 2024

RIA

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In the dynamic landscape of digital music services, recommendation systems play a pivotal role, evolving in tandem with advances in artificial intelligence and machine learning. This research undertakes a comparative exploration of two distinct approaches to song recommendations: content-based filtering and K-means clustering. Drawing upon an extensive Spotify dataset encompassing diverse song attributes like genre, tempo, and key, the study meticulously evaluates the efficacy of personalized track recommendations. Content-based filtering tailors recommendations to users' established preferences by scrutinizing audio features such as Danceability, Energy, and Loudness. Conversely, the Kmeans clustering algorithm groups' similar songs into clusters based on shared characteristics. The primary goal is to devise a music recommendation system that impeccably aligns with user preferences. The research evaluates the performance of the Kmeans clustering approach using the Silhouette index as a metric, revealing a recommendation accuracy exceeding 99%. Notably, data analysis underscores the superior performance of the content-based filtering technique. These findings hold substantial importance for refining personalized music recommendation systems, offering valuable insights into the effectiveness of different methodologies in catering to user-specific musical tastes. This study contributes to the ongoing evolution of digital music services, providing a foundation for future advancements in enhancing user experience through precise and tailored music recommendations.

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A community based cloud music streaming platform with recommendation and comments filtering

Yash Rahul,

et al. 2023

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

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Music Recommendation System Using Machine Learning

Cited by 8 publications

References 5 publications

Emotion-Based Music Movie and TV Series Recommendation System Using Deep Learning Algorithm

Emotion-Based Music Movie and TV Series Recommendation System Using Deep Learning Algorithm

Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches

A community based cloud music streaming platform with recommendation and comments filtering

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