StreamIt

Documentation

Abstract

The StreamIt platform is an innovative, AI-powered entertainment recommendation system designed to enhance the way users discover and interact with content across various media types. Built on the robust Next.js framework, StreamIt offers tailored recommendations for movies, TV series, and anime, addressing the challenge of navigating the vast and often overwhelming landscape of entertainment options available today.

With countless titles competing for viewer attention, users often struggle to find content that aligns with their unique tastes and preferences. StreamIt simplifies this experience by providing personalized content suggestions that not only match user interests but also adapt to their viewing habits over time. By leveraging a powerful AI model, the platform analyzes user preferences and viewing history through the integration of the Movie API, fetching detailed information like trailers, reviews, and ratings to enrich the user’s discovery experience.

Key Features

  • Dynamic User Interface: StreamIt features an intuitive and visually appealing interface, allowing users to easily input their preferences and enjoy a streamlined recommendation process.
  • Tailored Recommendations: Using advanced machine learning algorithms, StreamIt delivers AI-driven suggestions based on user behavior and past viewing history, ensuring recommendations are both relevant and personalized.
  • Comprehensive Content Information: Users can access a wealth of information for each recommendation, including trailers, ratings, and detailed descriptions, helping them make informed viewing choices.
  • Watchlist Management: A user-friendly feature that allows users to create and manage personalized watchlists, keeping track of shows or movies they want to watch.
  • Social Sharing & Community Engagement: StreamIt fosters a sense of community by allowing users to share recommendations with friends and family, enhancing engagement and enabling new content discovery through trusted sources.

What truly sets StreamIt apart from traditional recommendation systems is its unwavering focus on personalization and ease of use. By streamlining the process of discovering new entertainment, it enhances user engagement, fostering a more enjoyable viewing experience. In conclusion, StreamIt is more than just a recommendation tool; it is an interactive platform that adapts to individual preferences, providing an engaging and seamless content discovery journey.

Motivation

The entertainment industry has rapidly evolved, with streaming services presenting an overwhelming array of choices. While platforms like Netflix and Prime Video have transformed consumption habits, they often lack the desired personalization and control for users.

Project Motivation:

  • User Input: Users specify interests for targeted recommendations.
  • AI-Driven Suggestions: Recommendations evolve with user preferences.
  • Discovery Control: Users actively manage watchlists and preferences.
  • Mood-Based Recommendations: Suggestions adapt to various moods and genres.

The rise of streaming has resulted in an impersonal experience. StreamIt aims to restore user control, offering an AI-backed recommendation system for precise and customized content discovery. StreamIt addresses the need for improved content discovery in a crowded market, empowering users to engage more effectively.

SDLC Model

This section contains information about Methodology & Planning of Work.

Waterfall SDLC Model

Waterfall Model Image

This project will follow the Waterfall SDLC model, which is a linear and sequential approach to software development. In the Waterfall model, each phase must be completed before moving on to the next, ensuring a structured progression through the project. This model is ideal for the StreamIt platform, as it allows for thorough documentation and clear milestones.

  1. Requirement Analysis
    • User Interface and API Selection: Identify and select an appropriate API like the Gemini API for personalized recommendations.
    • Requirements Outline: Document functional (features) and non-functional (performance, security) requirements.
  2. System Design
    • User Interface Design: Create an intuitive UI for easy navigation and engagement.
    • API Integration Planning: Design how the chosen API will interact with the system.
    • Data Flow Diagrams: Develop diagrams to visualize data movement and entity relationships.
  3. Development
    • Frontend Implementation: Build a responsive UI using Next.js and Tailwind CSS.
    • API Integration: Connect to the Gemini API to enable personalized recommendation logic.
  4. Testing
    • Recommendation Accuracy Validation: Ensure recommendations align with user preferences.
    • Usability Testing: Gather user feedback to refine the experience.
  5. Deployment
    • Application Launch: Deploy the platform on a hosting service for public access.
    • Performance Monitoring: Track user feedback and application performance for ongoing enhancements.

Diagrams

This section contains various diagrams like UML diagrams for analysis

Data Flow Diagram Level 0

This diagram provides a high-level overview of the main processes and data flows in the StreamIt system, including the Explore feature.

Data Flow Diagram Level 0

Data Flow Diagram Level 1

This diagram expands on the DFD Level 0 by detailing the internal processes of the StreamIt system, including the Explore feature.

Data Flow Diagram Level 1

Activity Diagram

This diagram illustrates the flow of activities from user input to receiving content recommendations and managing the watchlist, including exploring movies.

Activity Diagram

Use Case Diagram

This diagram depicts the interactions between users and the system, highlighting the main use cases of the platform, including the Explore feature.

Use Case Diagram

Entity Relationship Diagram

This diagram shows the entities involved in the StreamIt platform and their relationships, including users, preferences, recommendations, watchlists, and the Explore feature.

Entity Relationship Diagram

Entities:

  • User, Watchlist, Content & Recommendation

Relations:

  • User ⇆ Watchlist (One to One) (Relation: Manages)
  • User ⇆ Content (One to Many) (Relation: View)
  • User ⇆ Recommendation (One to Many) (Relation: Receive)

Primary Keys:

  • User: userId
  • Watchlist: watchlistId
  • Content: contentId
  • Recommendation: recommendationId

Foreign Keys:

  • Watchlist: userId, contentId
  • Recommendation: contentId

Sequence Diagram

This diagram details the sequence of interactions between the user and the system when accessing the Explore feature and receiving recommendations.

Sequence Diagram

Class Diagram

This diagram outlines the classes within the StreamIt system, including their attributes and methods, while reflecting the Explore feature.

Class Diagram