- Goal: Learn about the different types of recommendation system algorithms and explore them in Jupyter notebooks.
- Dates: from 19 October to 2 November
- Where:
#project-of-the-weekin DataTalks.Club (get in slack here: https://datatalks.club/slack.html)
For more information about the "Project of the Week" initiative at DataTalks.Club, see README.md.
If you want to receive reminders about this event, sign up here
Note: this is a suggested list of technologies, you can chose alternatives instead
This is a proposed plan only, you don’t have to follow it day-by-day.
- Come up with a project idea.
- Select the dataset for your project.
- Create a GitHub project.
- Share your progress on Slack and in social media.
- Learn the basics about recommendation systems (see Suggested materials).
- Perform exploratory data analysis of your data in a Jupyter notebook
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
- 🗒️ Background section of Google Recommendation systems course
- 🏫 ML Zoomcamp - EDA section
- 🏫 Andrew NG - New ML Specialization on Coursera
- 📺 Matrix Factorization
- 🏫 Microsoft recommenders
- 🏫 Netflix matrix factorization Paper
Found good materials? Create a PR with links!
- Continue learning the basics about recommendation systems (see Suggested materials).
- Perform cleaning of your data in a Jupyter notebook.
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
Found good materials? Create a PR with links!
- Learn about Content-based recommendation systems.
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
- 🗒️ Content-based Filtering section of Google Recommendation systems course
- 📺 How to Build a Content-Based Recommendation System using Python
Found good materials? Create a PR with links!
- Continue learning about Content-based recommendation systems.
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
- 🗒️ Content-based Filtering section of Google Recommendation systems course
- 📺 How to Build a Content-Based Recommendation System using Python
Found good materials? Create a PR with links!
- Learn about Collaborative Filtering recommendation systems.
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
- 🗒️ Collaborative Filtering section of Google Recommendation systems course
- 🗒️ Build a Recommendation Engine With Collaborative Filtering
- 🗒️ An implicit feedback recommender for the Movielens dataset
Found good materials? Create a PR with links!
- Continue learning about Collaborative Filtering recommendation systems.
- Commit your changes.
- Share your progress on Slack and in social media.
Suggested materials
- 🗒️ Collaborative Filtering section of Google Recommendation systems course
- 🗒️ Build a Recommendation Engine With Collaborative Filtering
Found good materials? Create a PR with links!
- Continue exploring more about this topic
- Polish the documentation for your project
- Commit your changes.
- Share your progress on Slack and in social media
- Give us feedback
- Add the link to your project to this project of the week github page
- 🗒️ Google Recommendation systems course
- 🗒️ Introduction to recommender systems
- 🗒️ Recommender Systems: Machine Learning Metrics and Business Metrics
- 🗒️ Building Recommender Engines: Challenges and Opportunities
- 💾 Recommendation systems datasets on Kaggle
- 💾 Recommender Systems and Personalization Datasets
- 💾 Common Datasets Benchmark for Recommendation System
Materials legend:
- 🏫 Course
- 💾 Dataset
- 🗒️ Article
- 📺 Video tutorial
- 💻 Code
There are other things you can try:
- Learn about using Deep Neural Network Models for recommendation systems.
List of projects from the participants: