Fashion Recommendation System
by kandikits Updated: Mar 1, 2023
Recommendation systems are specialized algorithms and machine learning solutions that recommend or suggest similar products to consumers by analyzing many patterns involving user buying behavior, history, demographic metadata, etc.
Fashion Recommendation involves providing recommendations (recommending similar products) for any searched/viewed item in the fashion domain.
This kit illustrates the concept of Fashion recommendation in two different use cases.
- Recommendation of products based on text search (product name)
- Recommendation of products based on Image search (product Image)
The instructions for running a Fashion Recommendation Service created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
For Windows OS,
- Download, extract and double-click the kit installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 10 to 20 minutes based on network bandwidth.
- When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar
- To run the app manually, press N when you're prompted and locate the folder 'fashion-recommendation' in the "C://kandikits/fashion-recommendation" location
- Navigate into the directory 'fashion-recommendation'
- Open command prompt from this directory and run the 'run.bat' file. This would activate the virtual environment and open the jupyter notebook homepage automatically.
- Select the notebook 'Fashion Recommendation Service.ipynb' to open the Flask service implementation of Fashion recommendation.
For other Operating System,
- Click here to download python
- Click here to download the repository
- Extract the zip file and navigate to the directory 'fashion-recommendation'
- Run the following commands to install Python
tar -xf python*.tar.gz cd python3.* ./configure sudo make install
- Open the terminal in the extracted directory 'fashion-recommendation'
- Create and activate a virtual environment by these commands:
python3.9 -m venv example source example/bin/activate
- Install dependencies by executing the command
pip install -r requirements.txt
- Run the command ‘jupyter notebook’ and select the notebook 'Fashion Recommendation Service.ipynb' to open the Flask service implementation of Fashion recommendation.
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Fashion Recommendation App.
Libraries used in this solution
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Notebook is used for our development.
Jupyter Interactive Notebook
Jupyter Notebook 10143 Version:v7.0.0b3 License: Permissive (BSD-3-Clause)
Exploratory Data Analysis
These libraries are used for extensive analysis and exploration of data and to deal with arrays. They are also used for performing scientific computation and data manipulation.
The fundamental package for scientific computing with Python.
Python 23692 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.
An Open Source Machine Learning Framework for Everyone
C++ 175280 Version:v2.13.0-rc1 License: Permissive (Apache-2.0)
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Python 67579 Version:v2.0.1 License: Others (Non-SPDX)
scikit-learn: machine learning in Python
Python 54472 Version:1.2.2 License: Permissive (BSD-3-Clause)
The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.
matplotlib: plotting with Python
Python 17497 Version:v3.7.1 License: No License
Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.
Shell 3491 Version:72 License: Permissive (MIT)
The Python micro framework for building web applications.
Python 63166 Version:2.2.5 License: Permissive (BSD-3-Clause)
Kit Solution Source
Fashion recommendation is providing product recommendations in the Fashion domain. This repository illustrates the process of Fashion recommendations based on Input Product Text details and Product Image
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)