"CineSavvy: AI Personalized Recommendations"

A) Sentiment Analysis for Personalized Recommendations:

Employed machine learning techniques, such as natural language processing and sentiment analysis, to understand users emotional responses to movies. This System analyzes user reviews, comments, and interactions to determine sentiment (positive, negative, or neutral). This emotional context is then used to tailor movie recommendations to match users' preferences and moods.

B)Chat Bot and Movie Trivia for Movie Explorations:

Integrated a conversational chatbot to interact with users and assist them in discovering movies based on their preferences. The chatbot dynamically generates responses, relevant movies and providing additional details to enhance the exploration experience. Movie Trivia feature enhances user engagement by integrating an AI-driven trivia system that provides interesting and relevant information about movies.

C)Companion Finder for Binge-Watching Sessions:

Developed a feature that helps users find compatible companions for binge-watching sessions. Users can input their preferences, schedules, and viewing habits. The system then matches users with similar tastes and availability in the database that we have itself created in app(demonstration Purpose) fostering a sense of community and enabling collaborative binge-watching experiences.

D) Language and Cultural Filter Diversity Filter:

Implemented filters that consider language and cultural diversity, ensuring recommendations are inclusive and globally relevant. Users can set language preferences and filter recommendations based on cultural relevance. This feature enhances the platform's diversity, making it suitable for users with varying linguistic and cultural backgrounds.