Building a movie recommender system

The dataset is maintained by the "GroupLens research" and is available for free at

We will be working on the dataset of 20 million ratings ( This contains:

  • 20 million ratings
  • 465,000 tag applications applied to 27,000 movies by 138,000 users

We will work on an ALS recommender, which is a matrix factorization algorithm that uses Alternating Least Squares with Weighted-Lamda-Regularization (ALS-WR).

Let's consider that we have a matrix with users, u, and items, i:

Matrix, M (ui) = { r (if item i is rated by the user, u)
0 (if item i is not rated by user, u) }

Here, r represents the ratings submitted.

Consider that we have m number of users and n number of movies. ...

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