ABSTRACT
🧠Core Idea: Uses Natural Language Processing (NLP) to understand movie/series scripts for smart recommendations.
🔍 Problem Solved: Traditional OTT systems rely on surface-level data (ratings, genres, watch history), missing story depth.
đź“ś Our Approach: Analyze script, emotions, themes, and plot structures from scripts using advanced NLP.
🛠️ Tech Stack:
•Transformer Models (NLP & CNN)
•Sentiment Analysis
•Topic Modeling
•Content + Collaborative Filtering
🤝 Personalization: Matches extracted insights with user preferences to deliver context-aware suggestions.
EXISTING SYSTEM
Content Metadata Collection Every movie or series is tagged with metadata like:
⚬Genre (Action, Romance, Comedy, etc.)
⚬Language
⚬Release Year
⚬Cast, Director, etc.
User Profile Setup
User selects preferred genres or is automatically assigned based on watch history.
Matching Content by Genre
System filters and recommends content that matches the user’s preferred genres.
Static Recommendation List
Suggested content list remains mostly unchanged unless the user updates preferences.
DISADVANTAGES OF EXISTING SYSTEM
Recommendations are made based on genre tags like Action, Comedy, Drama, etc.
Users are grouped into fixed categories; everyone in a genre category sees similar suggestions.
Doesn’t analyze the actual content or storyline of the movie or series. Misses out on emotional tone, character depth, or plot structure.
Ignores evolving user preferences, mood, or nuanced interests. Often results in irrelevant or repetitive content.
PROPOSED SYSTEM
Script-Based Content Analysis
•Extracts and analyzes movie/series scripts to understand:
⚬Plot structure
⚬Emotions
⚬Themes
⚬Character relationships
Text Preprocessing & Tokenization
•Cleans scripts by removing stop words, punctuations, etc.
•Converts text into token sequences using NLP tokenizer.
Deep Learning Feature Extraction
•Uses a pre-trained NLP model with CNN to generate semantic embeddings for each script.
Similarity Calculation
•Compares embeddings using cosine similarity to find content most similar to the user’s past watched scripts.
Personalized Recommendation Generation
•Suggests movies or series that match the user’s content preferences, not just genre.
Continuous Learning
⚬Learns from user watch history and adapts to changing interests over time.
ADVANTAGES
- âś… Highly Personalized Recommendations
- âś… Deep Content Understanding
- âś… Adaptive to User Behavior
- âś… Admin Control and Automation
SOFTWARE & TECH STACK
- Backend: Python with Flask or Django
- ML Frameworks: scikit-learn, TensorFlow/Keras
- Chatbot Engine: Dialogflow / Rasa or custom NLU
- Frontend: React.js or HTML/CSS/JS
- Database: MySQL or MongoDB
HARDWARE REQUIREMENTS
- Processor: Intel i3 / AMD Ryzen 3 or higher
- RAM: Minimum 4 GB (8 GB recommended for ML training)
- Storage: 100 GB
📽️ DEMO VIDEO
INCLUDED PACKAGE
- Base Paper
- Complete Source Code
- Complete Documentation
- Presentation Slides (PPT)
- Flow Diagram (UML)
- Database File
- Screenshots
- Execution Procedure
- ReadMe File
- Add‑ons & Supporting Softwares
- Video Tutorials
SUPPORT & SPECIALIZATION
- Support via Ticketing System
- Voice Conference Assistance
- Video On Demand for Setup & Training
- Remote Connectivity Support
- Code Customization on Request
- Document Customization Assistance
- Live Chat Support
CONCLUSION
Smart Personalization Achieved
•DeepFlixAI successfully delivers context-aware and highly personalized recommendations by understanding the actual narratives and themes in scripts.
Overcomes Limitations of Traditional Systems
•Unlike genre- or rating-based systems, this project leverages deep learning and NLP to move beyond surface-level filtering.
Dynamic Learning & User Adaptability
•The model adapts to changing user preferences over time using watch history and behavioral analysis, improving long-term engagement.
Scalable for Real-World OTT Platforms
•The architecture is modular, secure, and scalable — suitable for integration into commercial OTT platforms with admin controls and auto-training.
đź“© SUPPORT
For source code access, customization, or guidance, contact us at: xpertieee@gmail.com
