Watch Recommendation

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  User Filters - 2

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  User Filters - 1

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  Recommendation and where to watch

Inspiration

The inspiration for Watch Recommendation stemmed from the overwhelming choice of movies and TV shows available across streaming platforms, leading to the paradox of choice. We wanted to develop a user-friendly app that makes personalized content discovery effortless and enjoyable, ensuring users can easily find content that matches their mood, favorite genres, and viewing history.

What it does

Watch Recommendation serves as the ultimate streaming companion, providing personalized movie and TV show recommendations. By leveraging user preferences, and advanced filters such as IMDB ratings, Rotten Tomatoes scores, release year, and language, the app ensures users discover content that precisely matches their tastes.

How we built it

The app is built on the core capabilities of Party Rock. This platform enabled us to develop a recommendation system that uses Gen AI to analyze user preferences. By integrating Party Rock's tools, we created an intuitive and personalized content discovery experience.

Challenges we ran into

We faced challenges with the limitations of Party Rock's models, including their outdated information, which made recommending current year movies difficult. The lack of web browsing capabilities and support for external API integrations restricted our ability to access real-time data and enhance the app's recommendation accuracy.

Accomplishments that we're proud of

Despite the challenges, we're proud of developing an app that simplifies the content discovery process, making it more personalized and user-friendly. We're also proud of our ability to navigate and creatively overcome the limitations of the tools at our disposal, resulting in a functional and innovative app.

What we learned

We learned about prompt engineering and the capabilities and limitations of Party Rock. This experience enhanced our skills in crafting effective prompts and deepened our understanding of AI's potential and constraints in content recommendation.

What's next for Watch Recommendation

Moving forward, we aim to overcome the current limitations by exploring alternative data sources and potentially developing our own model to support real-time data access and external API integrations. Our goal is to continuously improve the app's recommendation accuracy and user experience, making Watch Recommendation the go-to app for personalized content discovery.

Built With

• claude
• partyrock