The recommendation system is a classic application of machine learning that aims to predict which item a user will like best. Personalized recommendations play an integral role for e-commerce platforms, with the goal of driving user engagement through item recommendations.
In this workshop, we will build two types of recommendation systems using data from the MovieLens dataset:
- an item-item recommender using k Nearest Neighbors (kNN) and cosine similarity
- a top N recommender using matrix factorization
We will also cover the following topics on recommendations:
- collaborative vs. content-based filtering
- implicit vs. explicit feedback
- handling the cold start problem
- evaluation metrics
By the end of this workshop, you will have a better understanding of the different techniques and tools used to build recommendation systems in real-life scenarios.
- Python 3+
- pandas
- numpy
- scipy
- matplotlib
- seaborn
- scikit-learn
You will need to have (1) jupyter installed on your local machine, or (2) a gmail account to access Google Colab, which allows you to run jupyter notebooks in the cloud.