TrackMate
A smart music playlist app that provides a personalized music listening experience based on the users personal preferences
Summary
Trackmate is an AI-powered smart music playlist app that addresses the issue of music lovers struggling to discover new songs and artists that match their unique interests and personality. Many popular music streaming apps offer generic and unengaging playlists, but Trackmate continually learns from users' listening habits and preferences to provide a truly personalized music experience.
Problem Background
Popular music streaming apps like Apple Music and Spotify provide playlist recommendations based on algorithms and human curation that analyze a user's listening history, preferences, and interactions with the app. A study published in the Journal of Information Science in 2018 analyzed the accuracy of music recommendation algorithms used by popular streaming services like Spotify, Pandora, and Apple Music. The study found that while the algorithms were generally effective at recommending popular songs and artists, they were less successful at recommending more obscure or niche music that may be more relevant to individual user preferences.
Another study published in the Journal of Consumer Research in 2019 examined the user experience of music streaming services, including issues related to playlist recommendations. The study found that users often felt overwhelmed by the number of options available on these services and struggled to find music that aligned with their individual tastes and preferences. Users also reported frustration with the limited options for customizing playlists and the lack of diversity in the recommendations provided by the algorithms. As a result, users face the challenge of discovering new songs and artists that match their unique interests and personality. This highlights the need for a personalized music listening experience that can continuously add new songs based on factors like a user's age, mood, and personal tastes. This has led to the idea of creating a smart music playlist app that can address these pain points and provide users with a more engaging, diversified and personalized experience.
Goals
Goals include increasing listener satisfaction with regard to finding a recommended playlist and understanding what users look for in a quality playlist. The hope is to provide an alternative to the generic and unengaging playlists that are generated by popular music streaming apps by leveraging user preferences and advanced AI technology.
User Stories
- As a music lover, I want to have a personalized music listening experience that matches my unique interests and personality, so that I can discover new and diverse songs and artists that align with my personal preferences and engage me in a way that generic playlists cannot.
- Personas:
- Busy professional: A music lover who enjoys listening to music during their daily commute or while working. They have limited time to curate their own playlists and are looking for a music streaming service that can provide personalized recommendations that align with their interests and preferences.
- College student: A young adult who is passionate about music and enjoys discovering new artists and songs. They have a diverse tastes in music and are looking for a streaming service that can provide personalized recommendations that align with their unique interests and personality.
Needs
- Personalized music recommendations based on unique interests and personality
- Ability to discover new and diverse songs and artists
- Playlists that align with personal preferences
- Engaging music listening experience
Problem statement
Music enthusiasts struggle to discover new songs and artists that align with their unique interests and preferences. While popular streaming services like Apple Music and Spotify use algorithms to suggest recommendations, studies indicate that these suggestions are often too generic and fail to captivate users. Additionally, the recommendations lack diversity, making it challenging to discover artists and songs from different parts of the world. As a result, users are forced to manually search for new music, which is time-consuming and can lead to missed opportunities.
Proposed Solution
The proposed solution is to create a smart music playlist app powered by AI that can search for songs based on factors like specific tempo, mood, popularity, and personal tastes. The app continuously adds new songs that users will love based on their unique listening habits and attributes. In addition, users have access to Trackmate analytics that analyze each users unique listening habits to help the algorithm improve. This app aims to provide an alternative to the generic and unengaging playlists generated by popular music streaming apps, by leveraging user preferences and advanced AI technology to create personalized music listening experiences that match each user's unique interests and personality. The app will use data such as user listening history, liked and disliked songs, and other preferences to make recommendations that are tailored to each individual user.
Measuring Success
Co.Lab Success Metrics
By Demo Day we hope to have captured enough data to deliver a product that is fully functional and bug-free, as well as integration with popular streaming services. In addition, access to comprehensive user testing and feedback.
Product Success Metrics
- User engagement: the frequency and duration of user sessions, indicating how much time users spend on the app and how actively they use it.
- User retention: the percentage of users who continue to use the app on a regular basis.
- Personalization accuracy: This metric measures the accuracy of the app's personalized music recommendations based on user preferences and third-party integration with music platforms such as Spotify, Apple Music, and SoundCloud.
- User satisfaction: the results of user surveys and feedback, measuring how satisfied users are with the app and how well it meets their needs.
- Third-party integration success: This metric tracks the success of integrating with third-party services, measuring the percentage of successful API calls and user satisfaction with the integration.
- Playlist effectiveness: the number of new songs and artists discovered by users, as well as the overall quality of recommendations and playlist customization
- Number of active users: This metric tracks the number of unique users actively using Trackmate, indicating the popularity and usefulness of the app.
Learnings
Product Manager Learnings:
Gesner Charles Jr
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.