CineNiche Streaming Platform
A full-stack movie discovery platform built during BYU’s INTEX 2025 that enables users to browse, rate, and organize films while receiving personalized recommendations through content-based and collaborative filtering.
Project overview
CineNiche is a full-stack movie discovery platform developed during BYU’s INTEX 2025 capstone experience. The application enables users to browse, search, rate, and organize films through personalized watchlists while receiving movie recommendations based on viewing behavior and movie attributes. The platform features a responsive React and TypeScript frontend connected to a .NET Web API backend. Azure services, including Blob Storage, were used to manage movie datasets and media assets. Users can dynamically filter movies, create watchlists, and rate films while the system continuously updates personalized recommendations. My primary contributions focused on UX design and recommendation system development. I helped design the user experience of browsing along with the admin dashboard. I also contributed to building the recommendation logic using both content-based and collaborative filtering approaches to surface relevant films to users. The result was a scalable movie discovery platform capable of managing thousands of film records while delivering a personalized streaming-style experience.
Problem
Movie discovery platforms often struggle to help users find films that match their personal preferences. Our goal was to build a system that allowed users to explore large movie datasets while receiving personalized recommendations based on their interests and rating history.
Approach / process
Our team designed a full-stack architecture that separated the frontend user experience from the backend recommendation and data services. We built a responsive React interface to allow users to browse, filter, and rate films while integrating a .NET Web API backend that handled authentication, movie data management, and recommendation logic. We also designed recommendation algorithms combining collaborative filtering and content-based filtering to surface relevant films based on user behavior and movie metadata.
Implementation details
The frontend was built using React, TypeScript, and Bootstrap to create a responsive and interactive movie browsing experience. The backend was developed using a .NET Web API that managed user authentication, movie ratings, and recommendation generation. Azure services, including Blob Storage, were used to store large movie datasets and media assets. I contributed primarily to the UX design of the movie browsing and watchlist experience while helping implement the recommendation logic used to suggest films based on user ratings and movie similarities.
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