Abstract

With the exponential growth of Over-the-Top (OTT) platforms, users are often overwhelmed by the sheer volume of content available, leading to challenges in discovering relevant movies and TV shows. This research paper presents a personalized OTT content discovery app developed using Next.js, Tailwind CSS, and the TMDB API. The application aims to enhance user experience by providing tailored recommendations based on individual preferences and viewing history. Leveraging the comprehensive data provided by the TMDB API, the app enables dynamic and responsive user interfaces for seamless navigation across content categories. The combination of Next.js for server-side rendering and Tailwind CSS for modern, responsive design ensures optimal performance and user experience. The proposed system effectively addresses the current limitations in content discovery by offering a highly customized, easy-to-navigate platform that improves user satisfaction. Initial testing shows promising results in recommendation accuracy and user engagement, with future plans to integrate advanced machine learning techniques for even more precise recommendations.

Introduction

I. INTRODUCTION

The growth of OTT platforms such as Netflix, Amazon Prime, and Disney+ has changed the way users engage with entertainment by offering a wide variety of movies, TV shows, and documentaries. However, this rich content often leads to “content overload,” where users struggle to find new videos or movies that match their interests. As users seek more personalized experiences on these platforms, the challenge of content discovery has also become more intense. Different interests. This points to the need for a more versatile and personalized approach to content discovery. To address this issue, we propose that individual OTT content explores the web using cutting-edge technology and integrates a wide range of APIs for data content in a timely manner. Server-side design processing ensures high performance and user compatibility across multiple devices. 

Tailwind CSS is used in responsive and modern design, making navigation intuitive and visually appealing. The platform collects detailed information from The Movie Database (TMDB) API, which provides comprehensive information about movies, TV shows, genres, and ratings. Using this information, the website can provide personalized recommendations based on the user’s viewing habits and preferences.

II. PROBLEM STATEMENT

With the expansion of OTT platforms such as Netflix, Amazon Prime, Hulu, and Disney+, users are now inundated with content from multiple subscriptions. While each platform has unique content, managing multiple OTT subscriptions can be expensive and cumbersome. Users often face the problem of “content overload” and have to sift through large libraries to find videos or movies. Moreover, subscribing to multiple platforms to access multiple content is not only a financial burden, but also difficult to manage in terms of billing, APIs, and customer service accounts. OTT platforms only offer recommendations based on their own content, resulting in limited discovery options.

Users often miss out on content on other platforms unless they manually search. There is no integration that can aggregate content from multiple OTT services and provide personalized recommendations, which can make users feel isolated and conflicted. This creates a gap in the market for a platform that provides users with personal, affordable, and easy management.

To solve these problems, this study introduces a personalized OTT content discovery website that provides a unified platform for content discovery across platforms and enhances people's enjoyment of recommendations and seamless management.

III. OBJECTIVES

The primary objective of this research is to develop a personalized OTT content discovery website that addresses the challenges users face in managing multiple streaming subscriptions and finding relevant content across platforms. The website aims to:

Provide a unified platform for discovering content from various OTT services, enabling users to search for movies and TV shows from different sources in one place.

Enhance content discovery by offering personalized recommendations based on user preferences, viewing history, and genre interests.

Simplify subscription management, allowing users to track their subscriptions and manage billing cycles more efficiently.

Offer a cost-effective solution by reducing the need for multiple platform subscriptions, providing insights into where content is available, and helping users make informed decisions about which services to subscribe to.

Improve user experience through a responsive, easy-to-navigate website built using Next.js and Tailwind CSS, with content data sourced from The Movie Database (TMDB) API for comprehensive content information.

This project aims to bridge the gap in the OTT market by creating a solution that aggregates content discovery while offering personalized, streamlined user experiences.

IV. PROPOSED SYSTEM

The proposed system is a personalized OTT content discovery website designed to address the challenges of managing multiple OTT subscriptions and content overload. The system integrates modern web development technologies and an external content database to offer a seamless and efficient user experience.

A. Architecture

The website is built using Next.js, a React-based framework that supports server-side rendering for fast load times and optimized performance. This ensures that the site delivers a responsive and dynamic user experience across devices.

Tailwind CSS is utilized for styling, allowing for a modern, customizable, and responsive design that enhances usability and user interaction.

B. Content Aggregation

The system leverages The Movie Database (TMDB) API to aggregate extensive information on movies and TV shows from multiple OTT platforms. This includes details such as titles, genres, ratings, cast information, and availability across various streaming services.

By pulling content from a central API, the website provides users with a comprehensive view of available shows and movies, eliminating the need to switch between different platforms.

C. Personalized Recommendations

The system includes a recommendation engine that uses user data, such as viewing history, preferences, and ratings, to generate tailored suggestions. This enhances content discovery by offering more relevant and personalized recommendations compared to standard OTT platforms. Users can also filter content by genre, rating, or streaming service to further refine recommendations based on their specific interests.

D. Subscription Management

To address the complexity of managing multiple OTT subscriptions, the proposed system offers a subscription management feature. This allows users to track their active subscriptions, view upcoming billing cycles, and receive notifications about renewal dates.

The website also helps users identify where specific content is available, assisting them in making informed decisions about which services to subscribe to or cancel, ultimately reducing the cost of multiple subscriptions.

E. User Experience

The user interface is designed to be intuitive and easy to navigate, providing users with an organized view of personalized content recommendations, subscription details, and aggregated content from different platforms. Features like watchlists, user ratings, and favourites are included to help users easily organize and manage their content preferences.

F. Scalability and Future Enhancements

The system is designed to be scalable, with plans for future integration of additional APIs for expanded content aggregation across more OTT platforms.

Future enhancements could include the integration of machine learning algorithms for more sophisticated recommendation techniques and the incorporation of real-time updates from OTT platforms.

V. METHODOLOGY

The development of the personalized OTT content discovery website follows a structured approach using modern web technologies. The website architecture is designed to be modular, scalable, and easily maintainable, with the front-end developed using Next.js, styled with Tailwind CSS, and integrated with The Movie Database (TMDB) API for fetching movie and TV show data. The methodology focuses on the organization of the application, its components, and future scalability for additional features, such as subscription management.

A. Technology Stack

Next.js: The website is built using Next.js, a React-based framework that enables server-side rendering (SSR) and static site generation (SSG). This ensures fast load times and improves SEO by delivering pre-rendered pages to the user.

Tailwind CSS: The website's styling is done using Tailwind CSS, a utility-first CSS framework that allows for responsive design and custom styling across the website.

TMDB API Integration: The website integrates with the TMDB API to retrieve dynamic data, including trending movies, TV shows, genres, and detailed media information. This API allows the system to offer real-time content updates and personalize recommendations.

B. File Structure and Component Breakdown

    app/: This directory contains the main pages of the application, including:

        trending/: Displays trending content fetched from the TMDB API.

        movie/: A dedicated page for movies.

        series/: A page for TV series.

        search/: Implements the search functionality to query content based on user input.

        page.js: The main entry point for routing to different content pages.

    components/: This folder organizes reusable UI components to maintain modularity and scalability.

        display/: Handles the display logic for movies and TV shows.

        filter/: Implements filtering options for content based on criteria such as genres or ratings.

        navbar/: The navigation bar component for routing between sections like movies, series, and trending.

        pagination/: Manages paginated content loading, enabling smooth navigation through large data sets.

        search bar/: Implements the search input component to query content from TMDB.

        info/, title/, footer/: Additional components for displaying metadata, page titles, and the footer layout.

    public/: Contains static assets such as images used in the UI (e.g., movie cards or episode images).

C. Data Flow and API Integration

The website makes asynchronous calls to the TMDB API to fetch movie and TV show data. This data is dynamically rendered on pages like trending, movie, and series using SSR to ensure optimized performance.

The API responses are parsed and processed in various display components, which present the content to users in a structured and user-friendly manner.

D. Routing and Navigation

Next.js provides dynamic routing features, which allow users to navigate seamlessly between different content pages (e.g., trending, search, movie details) without page reloads. The app directory contains page-level components such as page.js and layout.js, which control the routing and layout structure.

The navigation bar (navbar/) ensures that users can easily switch between different sections of the website like trending, movies, and series.

E. User Experience and Design

The website is built with a mobile-first approach, ensuring responsiveness and smooth interaction across devices using Tailwind CSS. Interactive components like search bars, pagination, and filters help users efficiently discover new content.

Emphasis is placed on a clean, user-friendly interface that allows users to explore content categories intuitively while maintaining a minimalistic design aesthetic.

F. Subscription Management (Future Implementation)

Although the current implementation does not include a database or a subscription management system, the architecture is designed to be scalable for future additions. The subscription management feature will allow users to track their OTT platform subscriptions and manage their billing cycles. A backend database will be introduced to store user subscription data and preferences.

G. Scalability and Future Enhancements

The modular architecture of the website allows for easy integration of new features. In future updates, a backend service using Node.js or Flask can be developed for user management, enabling users to log in, save their watchlists, and manage subscriptions.

Integration of a machine learning recommendation engine could enhance personalized content suggestions by analysing user behaviour patterns over time.

References

[1] TMDB API: Used to fetch movie and TV show data, including metadata like genre, release dates, and availability on streaming platforms at the beginning of a sentence: [2] Frameworks and Libraries [3] Next.js: Framework used for building the frontend. [4] Tailwind CSS: Utility-first CSS framework for styling. [5] Flask: Backend framework to handle API requests and database interactions. [6] SQLite: Database for storing user information. [7] JWT for secure user authentication and session management. [8] Vercel for deploying the frontend (Next.js).Reference: Vercel Documentation [9] Flask deployment on Heroku (or alternatives like AWS, Render). Reference: Heroku Flask Deployment Guide

Copyright

Copyright © 2024 Shantanu Neve, Neeraja Surati, Aayush Tarjule. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.