recommender-system-dojo: A Jupyter Notebook repository from craftsangjae

Objective

Code Kata is defined as an exercise in programming which helps hone our skill through practice and repetition. In machine learning programming, Code Kata for implementing ML algorithms is very important, becuase we can realize the details ( such as Data Sampling, Weight initialization, various training strategy ...) while implementing the algorithm.

I implement various algorithms using in recommendation system and organize them into scripts. I'll update one script each week.

If you have a good topic, feel free to leave it on the issue! I will try to implement it as much as possible!

How to do the Code Kada together? (set-up environment)

Do not worry! I provide the environment written as a docker image.

# Run it From the root project directory
docker-compose up -d

Rec-Sys Katas List

Mining Frequent Pattern(apriori) using pandas

Goals

Implement Apriori function to extract frequent itemsets
Implement function to generate association rules from frequent itemsets

Reference

mlxtend

Thompson Sampling For A/B Test

Goals

Implement Thompson Sampling Agent to find best choice for A/B Test.

Real-Time Collaborative Filtering using MinHash

Goals

Implement Item-based CF Algorithm perfoming real-time recommendations and real-time Updates.
two Ideas Included : (Below ideas were presented by YongHo-Ha)
- Minhash as a LSH
- Secondary Indexing

Reference