Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/r-mahesh45/reccomendation-system
This project involves building a recommender system using cosine similarity to measure the similarity between items or users. It leverages numerical data to provide personalized recommendations. The model focuses on extracting meaningful insights and improving decision-making processes.
https://github.com/r-mahesh45/reccomendation-system
numpy pandas-dataframe python3 scikitlearn-machine-learning
Last synced: 22 days ago
JSON representation
This project involves building a recommender system using cosine similarity to measure the similarity between items or users. It leverages numerical data to provide personalized recommendations. The model focuses on extracting meaningful insights and improving decision-making processes.
- Host: GitHub
- URL: https://github.com/r-mahesh45/reccomendation-system
- Owner: R-Mahesh45
- Created: 2024-03-07T09:59:31.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-01-04T15:44:10.000Z (about 2 months ago)
- Last Synced: 2025-01-04T16:29:02.491Z (about 2 months ago)
- Topics: numpy, pandas-dataframe, python3, scikitlearn-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 851 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Recommender System Using Cosine Similarity
This project implements a recommender system to suggest items based on cosine similarity scores. The system calculates similarity between items or users to recommend relevant content. It is particularly useful for applications like e-commerce, movie recommendations, or personalized content delivery.
---
## Problem Statement
The goal of this project is to build a recommender system using cosine similarity scores. The system calculates similarities between entities (e.g., users, items) to recommend the most relevant items based on their similarity to a target entity.
---
## Features
- Calculate cosine similarity between entities (rows or columns of a dataset).
- Filter and analyze a specific section of the similarity matrix (e.g., rows 100-200 and columns 100-200).
- Generate personalized recommendations for users or items based on similarity scores.
---
## Dataset
The project uses a sample dataset with rows and columns representing entities (e.g., users, items, or features). The dataset is processed to create a cosine similarity matrix for recommendations.
---
## Approach
1. **Preprocessing**: The data is preprocessed to ensure all entities are represented in a numerical format suitable for similarity calculation.
2. **Cosine Similarity Calculation**: Cosine similarity is computed for all entities in the dataset.
3. **Filtering**: A subset of the similarity matrix is extracted using `use_cos_df.iloc[100:200, 100:200]` for focused analysis.
4. **Recommendations**: The top-k similar entities are identified for each target entity based on the cosine similarity scores.
---
## Results
- Successfully computed cosine similarity for the given dataset.
- Extracted and analyzed the relevant section of the similarity matrix.
- Generated recommendations based on similarity scores.
---
## Technologies Used
- **Python**: For data processing and computation.
- **Pandas**: For dataset manipulation and filtering.
- **NumPy**: For numerical calculations.
- **Scikit-learn**: For cosine similarity computation.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/R-mahesh45/recommender-system-cosine-similarity.git
```
2. Install required dependencies:
```bash
pip install -r requirements.txt
```
---
## Usage
1. Preprocess your dataset and load it into the program.
2. Use the provided script to compute cosine similarity.
3. Filter the similarity matrix for specific rows and columns, e.g., `use_cos_df.iloc[100:200, 100:200]`.
4. Generate recommendations based on the similarity scores.
---
## Conclusion
This project demonstrates how cosine similarity can be effectively used to build a recommender system. The approach is scalable and can be applied to various domains, including retail, entertainment, and content personalization.