https://github.com/manjit-baishya-datascience/cross-domain-recomendation-system
This project implements a cross-domain recommendation system using datasets from multiple domains (movies, music, and books). The goal is to leverage user interactions across these domains to improve recommendation accuracy.
https://github.com/manjit-baishya-datascience/cross-domain-recomendation-system
cross-domain-recommendation machine-learning music-movie-book recomender-system
Last synced: about 2 months ago
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This project implements a cross-domain recommendation system using datasets from multiple domains (movies, music, and books). The goal is to leverage user interactions across these domains to improve recommendation accuracy.
- Host: GitHub
- URL: https://github.com/manjit-baishya-datascience/cross-domain-recomendation-system
- Owner: manjit-baishya-datascience
- Created: 2024-11-22T08:20:10.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-31T17:21:40.000Z (about 2 months ago)
- Last Synced: 2025-03-31T18:32:55.316Z (about 2 months ago)
- Topics: cross-domain-recommendation, machine-learning, music-movie-book, recomender-system
- Language: Jupyter Notebook
- Homepage:
- Size: 113 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Cross-Domain Recommendation System**
## **Overview**
This project implements a cross-domain recommendation system using datasets from multiple domains (movies, music, and books). The goal is to leverage user interactions across these domains to improve recommendation accuracy.
## **Project Structure**
This project is organized into several key components:
- **Data**: User-item interaction datasets for movies, music, and books.
- **Preprocessing**: Cleaning and structuring data for recommendation modeling.
- **Modeling**: Applying collaborative filtering techniques.
- **Evaluation**: Assessing the performance of the recommendation system.
- **Pipeline**: The complete workflow from data preparation to recommendation generation.
## **Data**
The dataset consists of user interaction data across three domains:
- **Movies**: User ratings for different movies.
- **Music**: User ratings for music items.
- **Books**: User ratings for books.
### **Data Loading**
Data is loaded from text files with tab-separated values. The key attributes include:
- **user_id**: Unique identifier for each user.
- **item_id**: Unique identifier for each movie, music, or book.
- **rating**: User-provided rating for the item.
## **Data Preprocessing**
Preprocessing steps include:
- **Filtering relevant columns** (user_id, item_id, rating).
- **Handling missing values** to ensure data consistency.
- **Merging datasets** to prepare cross-domain interactions.
## **Modeling**
A collaborative filtering approach is used for recommendations. The key steps include:
- **Building interaction matrices** to represent user-item relationships.
- **Applying matrix factorization techniques** such as Singular Value Decomposition (SVD).
- **Generating recommendations** based on learned user and item embeddings.
## **Evaluation**
The performance of the recommendation system is assessed using:
- **User interaction validation** to check recommendation relevance.
- **Comparing rating predictions** with actual user ratings.
## **Pipeline**
The end-to-end pipeline follows these steps:
1. Load and preprocess data from multiple domains.
2. Train a collaborative filtering model.
3. Generate and evaluate recommendations.
## **Testing the Model**
The trained model is tested with various user interactions to demonstrate its effectiveness.
### **Example Recommendations:**
- **User 1**: Recommended Movies - [Movie A, Movie B, Movie C]
- **User 2**: Recommended Music - [Song X, Song Y, Song Z]
- **User 3**: Recommended Books - [Book P, Book Q, Book R]
## **Requirements**
To run this project, install the following Python libraries:
```bash
pip install pandas numpy scikit-learn
```