From fed9c49698dc764abf69176c9d55b28531a12816 Mon Sep 17 00:00:00 2001 From: Lamine Gaye Date: Wed, 16 Oct 2024 07:37:10 +0000 Subject: [PATCH] Add Recommendation System project documentation --- Projects/3-Advanced/Recommendation_System.md | 39 ++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 Projects/3-Advanced/Recommendation_System.md diff --git a/Projects/3-Advanced/Recommendation_System.md b/Projects/3-Advanced/Recommendation_System.md new file mode 100644 index 000000000..ce1c82f6e --- /dev/null +++ b/Projects/3-Advanced/Recommendation_System.md @@ -0,0 +1,39 @@ +# Recommendation System + +**Tier:** 3-Advanced + +A recommendation system is an essential tool in today's digital world, allowing users to discover items based on their preferences and history. This project aims to create a recommendation system that provides personalized suggestions based on user behaviors. + +### Constraints + +- Recommendations should not be based solely on random criteria; they must be calculated using machine learning algorithms. +- The system must handle real data and meet performance constraints. + +## User Stories + +- [ ] User can create an account and log in to save their preferences. +- [ ] User can see a homepage with personalized recommendations based on their history. +- [ ] User can view details of a recommended item, including a description, rating, and similar items. +- [ ] User can rate items and update their preferences, thereby influencing future recommendations. +- [ ] User can navigate through a list of recommended items sorted by relevance. +- [ ] User can delete their account and all associated data. + +## Bonus Features + +- [ ] User can receive recommendations based on items similar to those they have already viewed. +- [ ] User can share their recommendations on social media. +- [ ] User can view the history of their recommendations and interactions with the system. + +## Useful Links and Resources + +- [Introduction to Recommendation Systems](https://towardsdatascience.com/introduction-to-recommendation-systems-in-python-308b3e3e6c6e) +- [Collaborative Filtering](https://en.wikipedia.org/wiki/Collaborative_filtering) +- [Content-Based Filtering](https://en.wikipedia.org/wiki/Content-based_filtering) + +## Example Projects + +- [MovieLens Recommendation System](https://grouplens.org/datasets/movielens/) +- [Surprise: A Python library for building and analyzing recommender systems](https://github.com/NicolasHug/Surprise) +- [Netflix Prize Recommendation System](https://github.com/Netflix/Netflix-Prize) +- [RecSys Challenge](https://recsys.acm.org/recsys16/challenge.html) +