Spotlight

Project description

Our project was creating a website for promoting tickets based on users' Spotify music tastes. The reason for our stakeholders to want to try creating a website like this is that, as of right now, Spotify does not suggest events based on your music taste if your Spotify account is shared, for example, and our stakeholders found that it was simply sometimes just too inaccurate.

Context

Our project, Spotlight, operates at the intersection of digital entertainment and technology, specifically within the domain of music streaming and event promotion. In the digital age, music streaming services like Spotify have revolutionized how individuals access and enjoy music. However, there remains an untapped potential in leveraging these platforms for personalized event promotion.

This mobile-optimized website project is designed to bridge this gap by providing event recommendations based on a user's Spotify playlists. This initiative is particularly relevant, given the growing trend towards personalized digital experiences. Users increasingly seek services that understand their preferences and offer tailored content, including event suggestions that align with their musical tastes.

Our website addresses a significant limitation in existing music streaming services – the lack of personalized event recommendations, especially when shared accounts or algorithmic suggestions are not precise enough. By integrating with Spotify, we aim to offer a more nuanced and user-specific service, enhancing the overall music and event discovery experience.

Results

Our project Spotlight, at the forefront of integrating music streaming with personalized event promotion, has successfully developed two key products: a sophisticated event recommender system and a mobile-optimized website currently positioned at Technology Readiness Level (TRL) 7.

1. Event Recommender System:

This system is the cornerstone of our project, designed to analyze user data from Spotify playlists to provide tailored event recommendations. It adeptly navigates the complexities of diverse user music tastes, offering a more personalized experience than currently available on Spotify.
Extensive user testing has validated the system's effectiveness. The high accuracy in matching events to user preferences demonstrates the system's advanced capability in interpreting and utilizing complex user data. This aspect is crucial for success in the domain of digital personalization.
2. Mobile-Optimized Website:

Our website addresses the growing trend of mobile internet usage. It ensures wide accessibility and an optimal user experience on various devices. Based on user feedback, the website's design and functionality have been iteratively improved, enhancing its usability and user engagement.
Serving as the primary interface between users and our recommender system, the website effectively showcases the seamless integration of complex backend algorithms with a user-friendly front end.
3. Insights and Challenges:

The project has provided valuable insights into the challenges of recommending events based on musical preferences. It has highlighted the limitations of Spotify's current recommendation algorithms, especially in scenarios involving shared accounts or diverse musical tastes.
Our team successfully addressed challenges in data interpretation, underlining the importance of personalized algorithms and advanced data analysis in the digital recommendation space.

About the project group

Anna, Denislav, and Kelly are doing semester three open learning, Stijn is doing semester six open learning, and Raf is doing semester seven open learning. Their main experience from previous education is in media development within Fontys itself. In addition to media development, Raf has had some experience with AI development, and Stijn is a purely backend-focused software developer.
We had a stand-up every morning and assigned new tasks using the Jira environment after each sprint so that everybody knew what was expected of them. Together, we determined how long a task should take using Scrum poker. We would spend four days a week on the project and a single day on personal projects aside from this group project.