This is a code sample repository for online retail product recommendations using Collaborative Filtering (Memory-Based, aka History-Based). The source data used the famous Online Retail Data Set from UCI Machine Learning Repository.
- /data/Online Retail.xlsx <-- Online Retail Data Set from UCI Machine Learning Repository
- item_to_item_by_collaborative_filtering.ipynb <-- Product-Based Filtering, Product-to-Product product recommendations.
- user_to_user_by_collaborative_filtering.ipynb <-- User-Based Filtering, User-to-User product recommendations.
Below is a list of categories of Recommendation Systems to achieve different objectives.
- Collaborative Filtering
- Memory Based (aka History Based)
- Product-Based Filtering
- User-Based Filtering
- Model Based (e.g., Alternating Least Square (ALS))
- Hybrid
- Memory Based (aka History Based)
- Content-Based Filtering
- Hybrid
Enjoy!