Overview

30 mins - understand recommenders

10 mins - have a look at our project:

https://github.com/Cheukting/knn_recommender

50 mins - work on data preparation

50 mins - work on model

10 mins - try it out

What is a recommender?

• Based on the data about the users and items
• Make personalized recommendations
• Important to online business when there's no/ less human involved

Type of recommenders

• content-based systems
• collaborative filtering systems
• hybrid systems
  (just the combination of the 2 👍🏻)

Content-based systems

• If you have watched Cowboy Bebop the anime
• You probably would like Cowboy Bebop the live-action series
• (not always true 🤦🏻‍♀️)

Collaborative filtering systems

• If both you and I have watched a lot of anime
• If I watched Cowboy Bebop the anime
• You may like Cowboy Bebop the anime
• (there are popularity bias 🤷🏻‍♀️)

In our workshop

Data: ratings.csv and movies.csv from MovieLens Datasets (full version)

Method: collaborative filtering

Problem: Unpopular movies, inactive users -> sparse data points

Preparing the data

• there are long tails in the data
• many movies received only a few ratings (unpopular)
• many users only rate a few movies (inactive)
• we only want to use the popular movie and active users to reduce bias

Preparing the data

• do it in _prep_data
• create a filter for popular movies
• create a filter for active users
• only use those that are popular and active
• filter data that satisfied both filters

Preparing the data

• create a m x n array
• m: number of movies, n: number of users
• create a mapper from movie title to index
• the m x n array is sparse (not every user rate every movies)
• transform array to SciPy sparse matrix

Our model

• collaborative filtering
• user-based or item-based
• KNN model: similar movies are clustered together
• similar means received similar ratings from users
• feed the m x n array (sparse matrix) into KNN model
• inference and get the top n nearest neighbours

Our model

• using SciKit-learn NearestNeighbor:
  self.model = NearestNeighbors()
• parameters are set in set_model_params
• in _inference get the distances and indices of the top n_neighbours
• try different settings

Run our model in CLI

• run the Python script as a module with options
• Example:
  python knn_recommender.py --movie_name "Iron Man" --top_n 10
• Details about the options: see project repo
• after the data is processed it will be pickled for faster rerun (and used in PyScript version)

Run our model in browser (PyScript)

• make sure you have the pickle files (hashmap.p and movie_user_mat_sparse.p)
• start a local server: python -m http.server
• Open http://0.0.0.0:8000 and select knn_recommender.html from there

What have we learnt

• Know about the different types of recommenders
• Building a simple recommender
• See what data quality issue we may have
  • popularity bias: bias toward popular items
  • cold-start problem: new items do not get enough reviews

References and Credits

Kevin Laio (KevinLiao159) for original code and blog post

PyScript: https://pyscript.net/

Project repo: https://github.com/Cheukting/knn_recommender