Online Music Recommendation System

ABSTRACT:

With the rapid rise in digital content consumption and an ever-growing number of music listeners, personalized recommendation has become a vital feature in modern applications. Static music recommendation systems fail to adapt to the dynamic emotional states of users, thereby limiting user satisfaction. This project addresses that limitation by implementing a dynamic recommendation mechanism where each user receives different music suggestions based on the sentiments expressed in their recent tweets. The system continually updates the recommendation as the user’s mood evolves; ensuring that the music served aligns with their current emotional state.

The “Online Music Recommendation System” is a web-based application developed using Java as the core programming language, JSP for frontend development along with CSS and JavaScript, and MySQL as the backend database. This system is designed to offer a personalized music experience by dynamically recommending songs to users based on their emotional state.

The main objective of this project is to bridge the gap between users’ changing moods and music preferences by employing mood-based song recommendation through sentiment analysis of user-generated content, specifically tweets posted on the platform.

Overall, the system goes beyond traditional music streaming by incorporating real-time sentiment analysis to deliver dynamic, mood-specific music recommendations. It combines intelligent data processing with user-friendly design, creating a novel experience that not only entertains but also emotionally resonates with users.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

The existing music recommendation systems primarily focus on delivering song suggestions based on static data such as user listening history, song popularity, genre preferences, and collaborative filtering techniques. These systems generally collect information from user interactions like frequently played songs, liked tracks, search queries, and playlist contents, to build a profile and recommend songs that match those established patterns.
Most of the existing system platforms utilize either content-based filtering or collaborative filtering to generate recommendations. In content-based filtering, the system recommends songs that are similar to those the user has listened to in the past, by analyzing audio features such as tempo, pitch, rhythm, and genre. In collaborative filtering, the system suggests songs based on the preferences of users with similar listening behaviors.
Some advanced systems incorporate hybrid models that combine both content and collaborative filtering to improve the accuracy and relevance of suggestions. A few platforms also integrate basic forms of user feedback such as thumbs-up/down or rating mechanisms to refine recommendations over time. Additionally, large-scale commercial music platforms may use machine learning algorithms to fine-tune their suggestions based on vast amounts of user data and listening patterns collected globally.
The existing systems provide a structured and efficient way of delivering personalized music experiences and have been widely adopted due to their ability to learn user preferences over time and provide a seamless streaming experience across devices.

DISADVANTAGES OF EXISTING SYSTEM:

While existing music recommendation systems have significantly enhanced user experience by providing personalized suggestions, they also come with certain limitations that affect their effectiveness and adaptability.
Static Approach: One of the major drawbacks is their reliance on static data such as listening history, genre preferences, and user ratings. This approach often fails to capture the user’s current emotional state or changing preferences, leading to repetitive or irrelevant song recommendations over time.
Real-time adaptability: Another limitation is the lack of real-time adaptability. Most systems do not update recommendations based on a user’s mood or recent activity, making the suggestions less dynamic and emotionally resonant. For example, a user might prefer motivational music in the morning and calming music in the evening, but static systems typically cannot adjust to such temporal variations.
Filter bubble effect: Additionally, many systems depend heavily on collaborative filtering, which may lead to a “filter bubble” effect. This means users are only exposed to songs that are popular within their listening group, thereby limiting their exposure to diverse genres or new artists outside of their known preferences.
Incomplete Recommendations: Furthermore, content-based filtering techniques often require detailed audio analysis and metadata, which may not always be available or accurate, especially for newly released or less mainstream songs. This can result in incomplete or biased recommendations.
Lack of Sentiment Analysis: Lastly, most existing systems do not incorporate any form of sentiment analysis or natural language processing to understand user-generated content like social posts or comments. This limits their ability to truly personalize the experience based on a user’s real-time feelings or expressions, which are often more telling than historical data.

PROPOSED SYSTEM:

The proposed system, titled “Online Music Recommendation System“, introduces a novel approach by integrating real-time sentiment analysis to provide dynamic music recommendations tailored to the user’s current emotional state. The proposed system is developed using Java as the core programming language, JSP for the frontend along with CSS and JavaScript, and MySQL for the database, the system functions as a complete web application with both administrative and user modules.
Unlike traditional systems, the proposed model captures the mood of a user based on the content of tweets posted on the platform. These tweets are analyzed using sentiment analysis techniques, and the identified mood is then mapped to specific music genres. The recommendation engine then generates a playlist corresponding to the user’s present emotional condition. As a user posts new tweets over time, the system continuously updates its analysis, ensuring that the song suggestions evolve with the user’s mood.
The system includes two major entities: Admin and User. The Admin module provides functionalities such as adding and managing songs, viewing all added songs along with metadata like song genre, name, movie, music director, and associated media. It also includes features to manage user accounts, monitor user moods through sentiment analysis reports, manage the sentiment ruleset categorized into emotional and thematic genres (e.g., Devotional, Motivation, Sad, Love, Rock), and view graphical representations of song category distributions.
The User module supports account registration, login, and profile management. Once logged in, users can post tweets on their music wall, which the system analyzes to determine their mood. Based on this analysis, personalized song recommendations are displayed. In addition to music recommendations, the user interface supports social networking features such as viewing timelines, exploring the full song library, following other users, sending and receiving private messages, and tracking followers and following lists.
This integration of sentiment analysis with music recommendation, supported by an interactive and user-friendly interface, forms the core of the proposed system’s architecture and functionality.

ADVANTAGES OF PROPOSED SYSTEM:

The proposed Online Music Recommendation System offers several significant advantages over traditional recommendation platforms by introducing mood-based personalization and enhancing user engagement through real-time interactions.
Dynamic Music Recommendation: One of the key advantages of the proposed system is its ability to provide dynamic song recommendations. Unlike static systems that rely solely on past behavior, this system adapts in real-time to changes in a user’s mood, ensuring that the suggested music aligns with their current emotional state.
Mood Detection through Sentiment Analysis: The integration of sentiment analysis allows the system to understand the user’s mood based on the content of tweets posted on the platform. This enables a more natural and intuitive method of determining preferences without requiring manual input or survey-based feedback from the user.
Highly Personalized Experience: Since every user’s mood is unique and may vary frequently, the system generates distinct playlists for different users at different times. This personalized approach ensures that no two users receive the same recommendation unless they share the same sentiment at a given moment.
Continuous Adaptability: The system continuously monitors changes in user behavior through new tweets, which means that recommendations are not fixed but evolve with the user. This results in a more engaging and emotionally resonant experience as users discover new songs that match their changing moods.
Admin Control and Monitoring: The admin panel provides full control over the song database and user activities. Admins can manage songs, view user profiles, analyze mood reports, and monitor the distribution of songs across mood categories, enabling efficient content and user management.
Enhanced Social Interaction: The inclusion of social networking features such as timelines, following other users, and private messaging fosters community building and keeps users engaged within the platform.
Graphical Insight for Admin: The dynamic graph feature allows admins to visualize the number of songs available in each mood category, aiding in effective content curation and identification of underrepresented genres.
User-Friendly Interface: Built with JSP, CSS, and JavaScript, the system provides an intuitive and responsive interface that enhances the user experience across devices.
By combining music, mood analysis, and social interaction, the proposed system creates an intelligent and immersive platform that goes beyond conventional recommendation engines to deliver a meaningful and personalized music discovery experience.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 20 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : JAVA.
Frontend : JSP, CSS, JavaScript.
JDK Version : JDK 23.0.1.
IDE Tool : Apache Netbeans IDE 24.
Tomcat Server Version : Apache Tomcat 9.0.84
Database : MYSQL

Java Projects

Online Music Recommendation System