PersonaReel is an advanced content recommendation system developed as an academic project for the Data Structures and Algorithms (DSA) course. It leverages various data structures and algorithms to provide personalized reel and ad recommendations to users based on their interests and interactions.
- Personalized reel recommendations based on user interests and interaction history
- Targeted ad suggestions
- User interaction tracking (like, dislike, watch)
- Machine learning integration for improved predictions
- Heaps: For efficient retrieval of reels and ads with highest relevance scores
- Graphs: Used for linking reels to users and managing interaction matrices
- Linked Lists: Doubly Linked List (DLL) for managing interaction history
- Sets: For storing and passing reels and their weights
- Dictionaries: For storing reels and their associated weights
- Max Heap: For prioritizing recommendations
- Random Forest: For predicting user preferences (in machine learning improvement)
main.py: Core recommendation engine and user interaction loopDLL.py: Implementation of Doubly Linked Listmaxheap.py: Implementation of Max Heap data structureml-improv.py: Machine learning improvements using Random Forest
- Follow the on-screen prompts to interact with reels and ads.
- Use commands like 'L' for like, 'D' for dislike, 'N' for next, and 'Q' to quit.
The ml-improv.py file contains additional improvements using machine learning:
- Predicts whether a user will like a specific type of reel
- Uses Random Forest algorithm for classification
- Provides confusion matrix visualization for model evaluation
- Integration of more advanced recommendation algorithms
- Enhanced user profiling and preference learning
- Real-time data processing and recommendation updates
Vaibhav Shekar Shaun Sunny Sarath Chandra Rohit Vishnu Niramai Nayanar
This project was developed as part of the Data Structures and Algorithms course.