Behind “Moodies”: A Mood-Based Movie Recommendation Engine

How I Got the Idea for the Project

Movies have always been a great way to connect, relax, or explore different emotions. Finding the right movie can be challenging whether you’re in the mood for a light comedy or a gripping thriller. I’ve always found myself scrolling through endless lists on streaming platforms, often unsure of what to watch based on my current mood. This gave me the idea: why not create a web app that simplifies this experience?

I wanted something that would suggest movies based on how you’re feeling, what’s trending, and what streaming platforms you’re using. With a clear goal in mind, I set out to build a mood-based movie recommendation engine, where my journey began.

The Tools I Used

In any project, selecting the right tools is essential to making development smooth and scalable. Here’s a breakdown of what I used for this project:

• Next.js: A powerful React framework that offers built-in server-side rendering and static site generation. It was a perfect fit for creating a fast, scalable web app.
• TailwindCSS: For styling, I chose TailwindCSS due to its utility-first approach. It allowed me to quickly build responsive layouts without writing a ton of custom CSS.
• MongoDB: I used MongoDB to store and manage genres, moods, and movie data. The flexibility of MongoDB was ideal for this project since the data is quite dynamic.
• TMDB API: To get movie recommendations, I integrated with The Movie Database (TMDB) API, which provided me access to a massive database of movies and TV shows.
• shipfa.st: To accelerate my development process, I utilized the shipfa.st starter kit. This template provides a solid foundation for building modern web applications, including pre-configured Next.js, Tailwind CSS, authentication, payments, and more. By cloning this repo, I was able to skip much of the initial setup and focus on building my app’s unique features.

The Technologies I Implemented

Next.js is the backbone of the application. Its ability to perform server-side rendering helped me handle API calls to TMDB efficiently, ensuring a fast and dynamic user experience. Additionally, Next.js’s API routes were perfect for setting up back-end logic within the app, such as fetching moods and matching them to corresponding movie genres.

For the front-end, TailwindCSS streamlined my design process. Rather than writing CSS from scratch, Tailwind’s utility classes allowed me to quickly style my components while maintaining consistency across different screen sizes.

MongoDB served as the database to store the mapping of genres to moods. Each movie genre has an associated mood (e.g., action for “excited,” romance for “romantic”), and MongoDB’s schema flexibility helped me fine-tune this aspect without the constraints of a rigid relational model.

The TMDB API was the core data source for retrieving movies. I created custom endpoints to handle requests based on the user’s mood. By mapping moods to genres, I was able to send queries to TMDB’s movie discovery API to get tailored recommendations.

To ship my app quickly, shipfa.st allowed for smooth development. With its robust Next.js configuration and pre-configured authentication, payment, and analytics tools, I was able to focus on building out my app’s unique features.

The Challenges I Faced

1. Mapping Moods to Genres

One of the more complex challenges I encountered was creating an effective mapping between user moods and movie genres. Moods are subjective and nuanced, and boiling them down to a few movie genres wasn’t easy. I needed to strike a balance between offering enough options and keeping it simple for users. I created an object that mapped moods like “happy,” “sad,” or “adventurous” to corresponding movie genres, but testing different combinations took some time. I had to tweak the algorithm to get better recommendations and ensure it didn’t feel random.

2. Handling API Rate Limits

The TMDB API provides a wealth of data, but one downside was running into rate limits. During development, I often requested too many movies in quick succession, which led to failed API calls. To fix this, I implemented caching for common queries and added debounce functions to control the number of API requests being sent.

3. Creating an Intuitive UI

Since this app revolves around user moods, I wanted the interface to be highly intuitive and engaging. Choosing the right design language with TailwindCSS was critical. I iterated multiple times on how users select their mood — using emojis, sliders, or buttons — before settling on a simple button-based selection system that felt user-friendly. Responsive design was also crucial as I wanted the app to feel just as smooth on mobile devices.

4. Deployment and Scaling

Deploying the app was streamlined thanks to Vercel’s excellent support for Next.js. Vercel’s platform is optimized for Next.js applications out of the box, ensuring that both server-side rendering and client-side performance are handled efficiently. This made scaling the site from the beginning much easier.

Additionally, Vercel provides powerful utilities that enhance the Next.js ecosystem. For instance, @vercel/og simplifies the generation of dynamic Open Graph images, while Next.js Route Handlers offer a flexible way to create API endpoints. These tools, combined with Vercel’s seamless deployment process, significantly improved the development and deployment experience for the project.

The Lessons I Learned

1. Start Small and Iterate

I originally had grand ideas for this project, but quickly realized that starting with a simple MVP (Minimum Viable Product) was essential. By focusing on getting a basic version up and running, I was able to test my core concept — mood-based movie recommendations — without getting bogged down in unnecessary features.

2. APIs Are Powerful but Handle Them Wisely

APIs can be game changers for projects like this one, where real-time data is critical. However, they come with their own set of challenges, especially rate limits and data constraints. Implementing caching, as well as mindful handling of API requests, became crucial to the success of my project.

3. User Experience Is Everything

An idea can only go so far without the right user experience. I learned that small design choices, like how users select their mood, can make or break the app’s usability. TailwindCSS allowed me to experiment with different layouts quickly, which helped improve the app’s overall experience.

4. Stay Flexible with Your Database

MongoDB’s flexibility was a huge asset when dealing with complex mappings between moods and genres. Initially, I thought I’d need a more rigid database structure, but as I continued building, MongoDB allowed me to add new fields and modify the schema without a hitch.

Conclusion

Building this project was a rewarding experience, combining several tools and technologies I’ve been passionate about. By leveraging the shipfa.st template as a starting point, I was able to focus on the unique aspects of my recommendation engine while benefiting from pre-built essentials like authentication, payments, and a robust tech stack.

Whether you’re looking to build something similar or start a different type of web application, I encourage you to explore how APIs, flexible databases like MongoDB, and powerful frameworks like Next.js can be used together to create a seamless user experience. The shipfa.st template provides an excellent foundation, allowing you to rapidly prototype and launch your ideas while maintaining best practices in web development.

If you’re interested in trying out the mood-based movie recommendation engine, feel free to check it out! I’ll be iterating on the project and adding more features based on user feedback.

Thanks for reading, and I hope this helps you on your journey of building and launching your side projects!