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---
title: Recommender system and customer segmentation
emoji: 🐨
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.10.0
app_file: recommender_system.py
pinned: false
license: mit
---
# Recommender system and customer segmentation
Demo with recsys and clustering for the [online retail](https://www.kaggle.com/datasets/vijayuv/onlineretail?select=OnlineRetail.csv) dataset.
## Objective
Recommender system:
1. interactively select a user
2. show all the recommendations for the user
3. explain why we get these suggestions (which purchased object influences the most)
4. plot the purchases and suggested articles
Clustering:
1. compute the user clustering
2. plot users and their clusters
3. explain the meaning of the clusters (compute the mean metrics or literally explain them)
## Setup
In your terminal run:
```bash
# Enable the env
source .venv/bin/activate
# Install the dependencies
pip install -r requirements.txt
# Or install the freezed dependencies from the requirements_freezed.txt
# You are ready to rock!
```
## Run
In your terminal run:
```bash
streamlit run recommender_system.py
# Now the defualt browser will be opened with
# the stramlit page. It you want to customize the
# execution of streaming, refer to its documentation.
```
## Resources
- [streamlit](https://streamlit.io/)
- [implicit](https://github.com/benfred/implicit), recsys library
- [t-sne guide](https://distill.pub/2016/misread-tsne/)
- [RFM segmentation](https://www.omniconvert.com/blog/rfm-score/)
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