Recommender System (1) — Overview & Matrix Factorization technique

Recommendation System

  • items : products, movies, events, articles
  • users : service users, users, readers, customers

Collaborative Filtering vs Content-based Filtering

image source

Content-based Filtering (CBF)

Collaborative Filtering (CF)

Neighborhood Methods (NM)

Latent Factor Models (LF)

Matrix Factorization Techniques (MF)

  • like/dislike buttons
  • star ratings (ex)netflix
  • user viewed an item or item’s details
  • user added the items to the watchlist or cart
  • user purchased an item
  • user have read an article up to the end

The Basic Matrix Factorization Model

Adding Input Sources

Temporal Dynamics




Software Engineer KAIST CS & ID

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Hypothesis Testing (t-tests)

You’re leading People Analytics: Now what? Overrated vs Underrated

Clearly Explained: Ensemble learning methods, the heart of Machine Learning

Don’t Let Data Science Become a Scam

Why you Should Learn R in 2021

Getting Dirty with Dask [Part 1]

Bank Load Status Prediction

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Youngjae Jang

Youngjae Jang

Software Engineer KAIST CS & ID

More from Medium

How do the Restaurant’s attributes influence its rating?

Neural Architecture Search (NAS)

Challenge Rating: Is it the only thing a Monster is made of?

Advantages and Disadvantages of Tensorflow

Advantages and Disadvantages of Tensorflow