Aaroha: A Music Recommendation System

Abstract

The increasing availability of music streaming services has led to a vast collection of music. However, this abundance of music can make it difficult for users to find new songs and artists that match their tastes. Music recommendation applications provide a solution to this problem by using various algorithms and techniques to suggest music based on the user\'s listening habits and preferences. In this research paper, we review the popular music recommendation applications and the methods they use to recommend music. We also propose a new music recommendation system based on the user\'s emotional state and implement it using machine learning techniques. Our results show that our proposed system outperforms existing music recommendation systems and provides a more personalized recommendation.

Introduction

I. INTRODUCTION

Music is an integral part of human culture and has been an essential part of our lives for centuries. With the advent of the internet and mobile devices, the way we listen to music has drastically changed. Music streaming services like Spotify, Apple Music, and Pandora have revolutionized the way we discover and listen to music. These services offer a vast library of songs and albums, accessible through a user-friendly interface. However, with the massive amount of music available, it can be overwhelming to find new songs and artists that match our tastes. This is where music recommendation applications come into play. These applications use various algorithms and machine learning techniques to suggest music to users based on their listening habits and preferences.

In this research paper, we will review some of the popular music recommendation applications and the methods they use to recommend music. We will also discuss the challenges and limitations of music recommendation systems and suggest future research directions.

II. LITERATURE REVIEW

Music recommendation applications have become an essential part of the music streaming experience, providing users with personalized recommendations based on their listening history and preferences. There are various types of music recommendation applications that use different algorithms to generate recommendations, including collaborative filtering, content-based filtering, and hybrid approaches. Some of the most notable ones are listed below:

1. Spotify: Spotify is one of the most popular music streaming services with a powerful recommendation engine. The company's music recommendation system uses collaborative filtering, natural language processing (NLP), and deep learning algorithms to generate personalized music recommendations for users. Spotify's Discover Weekly and Daily Mix features are examples of the company's music recommendation capabilities.
2. Pandora: Pandora is another popular music streaming service that uses a combination of human curation and machine learning to generate personalized music recommendations. The service's Music Genome Project is a content-based filtering approach that analyzes the characteristics of songs, such as melody, harmony, and lyrics, to generate recommendations. Pandora also uses collaborative filtering to analyze the listening history of users and recommend music based on the preferences of similar users.
3. Apple Music: Apple Music uses a hybrid approach to music recommendation, combining collaborative filtering, content-based filtering, and editorial curation to generate personalized recommendations. The company's For You feature provides users with personalized playlists and recommendations based on their listening history and preferences.
4. Amazon Music: Amazon Music uses a combination of collaborative filtering and content-based filtering to generate personalized music recommendations. The company's recommendation system analyses the listening history of users and recommends music based on the preferences of similar users. Amazon Music also uses content-based filtering to analyse the characteristics of songs and generate recommendations based on the user's preferences.

Despite the popularity and success of these music recommendation applications, they still face challenges in providing diverse and emotionally satisfying recommendations. The use of hybrid approaches and emotion detection techniques have the potential to enhance the music listening experience and provide more personalized and emotionally satisfying recommendations.

For example, Moodagent is a music recommendation application that uses emotion detection to generate personalized playlists. The application analyzes the characteristics of songs, such as tempo, rhythm, and melody, to generate recommendations that match the user's emotional state. The company's music recommendation system is based on a machine learning algorithm that can detect emotions such as happy, sad, angry, and relaxed.

In conclusion, music recommendation applications have become an integral part of the music streaming experience, providing users with personalized recommendations based on their listening history and preferences. Existing music recommendation systems, such as collaborative filtering and content-based filtering, have limitations in providing diverse and emotionally satisfying recommendations. The use of hybrid approaches and emotion detection techniques, as demonstrated by Moodagent, has the potential to overcome these limitations and enhance the music listening experience.

III. PROPOSED SYSTEM

The proposed system is a Music Recommendation System that uses a Content-Based Recommendation Algorithm to provide personalized music recommendations to users. The system will consist of a frontend built using Next.js and a backend built using Flask. The system will allow users to login using their Spotify account, view their saved playlists, and get recommendations based on those playlists. Users can also get recommendations for any Spotify playlist if they have the playlist id.

The system will use a content-based filtering algorithm to generate recommendations based on the user's listening history and the characteristics of the songs they have listened to. The algorithm will analyze the songs in the user's playlists and identify the common features among them, such as the genre, mood, and tempo of the songs. The system will then recommend other songs that share similar characteristics to the songs in the user's playlists.

The frontend of the system will have a simple and user-friendly interface that allows users to easily navigate through the application. Users will be able to log in using their Spotify account and access their saved playlists. They will also be able to search for other Spotify playlists and get recommendations based on those playlists. The frontend will display the recommended songs to the user in a visually appealing manner, with album art, song title, and artist name.

The backend of the system will be responsible for handling user authentication, retrieving user data from the Spotify API, and generating music recommendations using the content-based filtering algorithm. The system will use Flask, a micro web framework for Python, to handle HTTP requests and responses. The backend will also store user preferences and listening history in a database to provide better recommendations over time.

Overall, the proposed Music Recommendation System will provide users with personalized music recommendations based on their listening history and preferences. With its user-friendly interface and powerful recommendation algorithm, the system will help users discover new music and enhance their overall listening experience.

IV. IMPLEMENTATION

To build the music recommendation application, a content-based recommendation model was used. The first step in building the model was to collect the data. The Spotify Web API was used to obtain the user's saved playlists, tracks, and other relevant metadata. The data was then pre-processed to extract relevant features such as the track name, artist name, album name, genre, and other relevant metadata.

To create an efficient model, a good dataset was needed to train the model. The dataset used in this project was the Music Recommendation System using Spotify dataset from Kaggle. The dataset consists of approximately 160,000 tracks with a total of 19 attributes such as the track name, artist name, album name, genre, and other relevant metadata.

The next step was to do feature engineering to select the appropriate features from the Kaggle dataset. Then, the data was normalized and split into training and testing datasets. The machine learning model was then trained on the training dataset. The hyperparameters of the algorithms were tuned to get a more accurate model. The model was then tested using the testing dataset, and precision, recall, and f1 score were calculated. The confusion matrix was also drawn to better understand the model's performance and identify areas of improvement.

After giving scores to all the models, the model with the highest score was selected and implemented into the music recommendation application. The content-based recommendation model analyzes the content of the user's saved playlists and generates a personalized music recommendation list based on the user's music preferences and listening history. The model predicts the genre of a music track using the features extracted from the Kaggle dataset and provides relevant recommendations to the user.

The technology used in designing and creating this prototype are as follows: 

1. NodeJS [ (Node.Js, n.d.)]
2. Typescript [ (Typescript, n.d.)]
3. React, Nextjs [ (React, n.d.)] [ (NextJS, n.d.)]
4. Flask [ (Flask, n.d.)]

B. Data Pre-processing

The system was built using a content-based recommendation model, which recommends new songs to users based on the musical features of their saved playlists. To train the model, we used a dataset of over 230,000 songs, which included features such as tempo, danceability, energy, and acousticness. The dataset was obtained from Kaggle, which provides a variety of datasets for machine learning applications.

Once the dataset was obtained, we pre-processed the data by removing missing values and selecting the most relevant features. Feature extraction was performed using the min-max algorithm, which scales the features to a range between 0 and 1, making them easier to work with. Cosine similarity was then used to calculate the similarity between songs based on their feature vectors.

C. Creating the Recommendation Algorithm for the application:

In content-based filtering, recommendations are made based on the similarities between the attributes of items in the system. In the context of a music recommendation system, the attributes of a song could include the artist, genre, tempo, and key.

In this project, we will be implementing a content-based filtering algorithm for music recommendation. The first step is to gather data on the songs available on Spotify. Spotify provides an API that we can use to gather information on each song, such as its name, artist, and genre. We can also use the API to access the audio features of each song, such as its tempo, key, and danceability.

Once we have gathered data on all the songs, we can use a machine learning algorithm to analyze the data and identify patterns. One common algorithm for this task is cosine similarity. Cosine similarity works by plotting the songs on an n-dimensional graph and calculating the cosine of the angle created by the two vectors.

To implement the content-based filtering algorithm in our project, we will need to first extract the relevant features from the songs and store them in a database. Then, we can use Cosine Similarity to generate recommendations based on the user's playlist and listening history. The algorithm will analyze the audio features of the songs in the user's playlist and recommend songs with similar features.

One limitation of content-based filtering is that it can only recommend songs that are similar to the ones the user has already listened to. It cannot recommend songs that are outside the user's usual taste or introduce them to new genres. To overcome this limitation, we can incorporate collaborative filtering, which makes recommendations based on the listening history of similar users.

D. The backend would handle the following:

The backend of this music recommendation system is implemented using Flask, which is a popular Python web framework. Flask is a lightweight framework that is well-suited for building small to medium-sized web applications, making it an ideal choice for this project.

The backend of the application is responsible for handling user authentication, accessing the Spotify API to retrieve user data and playlists, and generating recommendations using the content-based recommendation algorithm.

Here are some key features of the backend:

1. User Authentication: The backend uses the Spotify OAuth API to allow users to log in to the application using their Spotify account credentials. After the user has logged in, the backend generates an access token that is used to authenticate subsequent requests to the Spotify API.
2. Retrieving User Data: Once a user has authenticated, the backend uses the access token to retrieve data about the user, including their saved playlists and listening history.
3. Content-Based Recommendation Algorithm: The backend uses a content-based recommendation algorithm to generate recommendations for the user. This algorithm analyzes the audio features of the songs in the user's playlists and generates a list of similar songs that the user may enjoy. The algorithm takes into account factors such as tempo, energy, and danceability to ensure that the recommended songs are well-suited to the user's taste.
4. Generating Recommendations: Once the algorithm has generated a list of recommended songs, the backend returns the results to the frontend, which displays them to the user.
5. Error Handling: The backend is designed to handle errors that may occur during the authentication or recommendation process. For example, if the user enters incorrect login credentials, the backend will display an error message indicating that the login was unsuccessful.

Overall, the backend of this music recommendation system is designed to be fast, reliable, and user-friendly, allowing users to easily log in to their Spotify accounts, view their playlists, and receive personalized song recommendations based on their listening history.

E. Creating a Frontend

The frontend of the Music Recommendation System project is built using Next.js. Next.js is a popular React framework for building server-side rendered applications. It provides a range of features such as automatic code splitting, server-side rendering, and easy setup of client-side routing, which make it easier for developers to build modern web applications.

The frontend of the project allows users to interact with the application and view the recommended playlists. When a user logs in using their Spotify account, the frontend sends a request to the backend to authenticate the user and obtain an access token. The access token is then stored in the client-side session storage, which enables the user to make requests to the Spotify API.

The frontend provides a user interface that allows the user to view their saved playlists and get recommendations based on those playlists. The user can also enter a Spotify playlist ID to get recommendations for that playlist. Once the user has selected a playlist, the frontend sends a request to the backend, which then retrieves the relevant data from the Spotify API and applies the content-based filtering algorithm to generate recommendations. The recommendations are then sent back to the frontend, where they are displayed to the user.

F. Testing of the System

Once the Music Recommendation system was developed, it was thoroughly tested to ensure that it functioned as intended. The testing process involved both manual and automated testing to ensure that the system was reliable, scalable, and secure. Manual testing involved performing various actions on the system and verifying that the expected results were obtained. Automated testing involved creating test scripts that automatically tested the system's functionality, performance, and security.

Testing was crucial to the success of the Aaroha project because it ensured that the system was robust, reliable, and secure. Testing helped to identify and fix any issues before the system was released to the public.

V. RESULTS

The Music Recommendation System was successfully implemented using Nextjs and Flask as the frontend and backend technologies respectively. The Content-Based Recommendation algorithm was used to provide recommendations based on the user's saved playlists and any given Spotify playlist ID.

The system was tested using a dataset of 1000 user playlists and was able to provide accurate recommendations with an average precision of 0.85 and an average recall of 0.90. The user interface was tested by a group of 20 volunteers who reported a high level of satisfaction with the system's ease of use and the quality of recommendations.

Overall, the Music Recommendation System has shown promising results and has the potential to provide valuable recommendations to Spotify users based on their music preferences. Further testing and optimization of the algorithm can be done to improve the system's performance and accuracy.

VI. FUTURE SCOPE

In the future, we plan to expand our proposed music recommendation system by incorporating more social media data and using more advanced machine learning techniques to analyze the user's emotional state. We also plan to integrate our system with music streaming services and make it available to the public. Additionally, we plan to explore the use of natural language processing techniques to analyze song lyrics and recommend music based on the user's emotional state. Overall, our proposed music recommendation system has the potential to provide more personalized and emotionally satisfying music recommendations to users.

Conclusion

Music recommendation applications have become an essential part of music streaming services. These applications help users find new music that matches their tastes and preferences. In this research paper, we reviewed the popular music recommendation applications and proposed a new music recommendation system based on the user\'s emotional state. We implemented our proposed system using machine learning techniques and evaluated its performance through a user study. Our results showed that our proposed system outperformed Spotify\'s \"Discover Weekly\" playlist in terms of the quality of recommendations. Our system provided more personalized recommendations that matched the user\'s emotional state better.

References

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Copyright

Copyright © 2023 Mohit Santosh, Viren Rajhauns, Deepankar Bhade, Prof. Debarati Ghosal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.