MovieMatch

Adding a Movie to "Favorites" List
Scrolling Through Movies

Inspiration

Our team's shared love for movies inspired us to create MovieMatch. We often found it challenging to decide on a movie to watch together, so we wanted to build an app that could help us and others in similar situations.

What it does

MovieMatch allows users to create groups with friends, curate a list of favorite movies, and receive personalized movie recommendations based on the group's preferences.

How we built it

We built MovieMatch using React Native for the frontend to ensure cross-platform compatibility. For the backend, MovieMatch is powered by Convex, providing scalability and efficiency. We also integrated third-party APIs like IMDb using Postman for movie data and recommendations.

Postman Workspace Link: link

Challenges we ran into

As it was our first time working with React Native, creating a full-stack, and integrating APIs, we faced a heap of challenges. Managing these learning curves while ensuring the app's functionality and performance was a significant challenge. Using services like Postman and Convex, tech that is built for ease-of-use and rapid development, helped us immensely.

Accomplishments that we're proud of

We're proud of successfully implementing real-time syncing of group preferences and generating accurate movie recommendations. We also managed to create a user-friendly interface that made it easy for users to create groups and add movies to their lists. Getting past that first step of getting the stack to compile and subsequently building upon it was extremely rewarding.

What we learned

Through this project, we learned a lot about integrating third-party APIs, managing real-time data updates, and optimizing performance in a mobile app. We also improved our teamwork and communication skills, which were crucial for collaborating effectively in a fast-paced hackathon environment.

What's next for MovieMatch

In the future, we plan to add more features to MovieMatch, such as user profiles, ratings, and reviews. We also want to enhance the recommendation algorithm to provide even more personalized suggestions based on individual user preferences. Additionally, we aim to improve the overall user experience by adding more interactive elements and refining the design.