MovieMate is an innovative movie recommendation application that leverages cutting-edge technologies to provide personalized movie suggestions tailored to the preferences of its users. By combining dynamic pagination, web scraping, machine learning, and natural language processing, MovieMate delivers a seamless and intuitive user experience.

• Dynamic Pagination: Movie recommendations are tailored based on the number of persons intending to watch the movie, ensuring personalized suggestions for every user scenario.

• Web Scraping: Utilizing web scraping techniques, MovieMate extracts relevant data related to movies from various sources, enriching its recommendation database with up-to-date information.

• Pretrained Model Embedding: Each movie in the database is embedded into a 1024-dimensional vector space using a pretrained model (e5-large-v2), enabling efficient similarity calculations for recommendation purposes.

• Vector Database: Movie data, represented as embedded vectors, is stored in a vector database (Supabase), facilitating quick and scalable access to movie information.

• Flask Server: A Flask server manages the flow of data between the mobile app and the inference engine, handling REST API requests and responses effectively.

• Machine Learning Inference: User inputs, including preferences and watch duration, are processed by Python functions to generate embedded vectors, which are then used to calculate cosine similarity scores for movie recommendations.

• Natural Language Processing with Langchain: Movie selections are further analyzed using Langchain, where queries are posed to generate insightful answers about the chosen movies, providing users with detailed explanations and insights.

• Poster Extraction: Movie posters are fetched using the OMDB API, enhancing the visual appeal of the app and providing users with additional context about the recommended movies.

1. Input Requirements: Users are prompted to provide details such as the number of viewers, duration of viewing, and individual preferences for each viewer.

2. Data Processing: User inputs are processed and concatenated into embedded vectors using the pretrained model.

3. Recommendation Generation: Cosine similarity scores are calculated between user vectors and the vectors of movies in the database. The top-scoring movies are recommended to the user.

4. Insightful Analysis: Langchain is employed to analyze user selections, generating comprehensive insights into the chosen movies and the reasons behind the recommendations.

5. Visual Presentation: Recommended movies, along with their posters and insights, are displayed to the user via the Flask server, providing an engaging and informative experience.

• Android Studio
• Python
• Flask
• Web Scraping
• Pretrained Model (e5-large-v2)
• Supabase (Vector Database)
• Hugging Face API
• OMDB API
• Langchain