A Competition-Aware Approach to Accurate TV Show Recommendation.

Project Code :TCMAKO1136

Objective

The main objective is to develop an Android-based application that recommends TV series based on popularity, genre, and user interaction, while enabling secure login, personalized favorites, and smooth navigation across summaries, seasons, and episodes for a simplified discovery experience.

Abstract

The rise of digital streaming has led to an overwhelming number of TV shows, making content selection increasingly challenging. ShowNix addresses this issue by offering an intuitive recommendation app tailored for series enthusiasts. The application provides a sleek interface where users can register, log in, and explore a personalized dashboard. It features two distinct viewing sections: a complete series catalog and a popularity-ranked list, enabling seamless discovery. Users can dive into detailed show summaries, explore seasons and episodes, and add favorites with a single tap. A powerful search function allows title-based or genre-specific browsing. Data is dynamically fetched using Retrofit API integration, while local user authentication and favorites are managed securely via Room Database. By integrating modern Android components, ShowNix creates a user-centric experience that prioritizes content relevance and ease of access. The platform encourages user engagement through its responsive navigation and provides intelligent sorting, positioning itself as a smart assistant in the vast world of entertainment.

Keywords: TV Show Discovery, User Preferences, Genre-Based Search, Ratings Filter, Android App

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.