San Diego State University

Movielens dataset contains millions of movie ratings data, namely, these are the combination of movie and user IDs and numerical rating. This dataset can be used to practice for recommendation problems. 

MovieLens Data for Collaborative Filtering

 ``` python
from fastai.collab import *
from fastai.tabular.all import *
path = untar_data(URLs.ML_100k)

ratings = pd.read_csv(path/'u.data', delimiter='\t', 
                                        header=None,
                                         names='user','movie','rating','timestamp'])


```

Load Data

There are some rows and columns with empty values and the goal would be for the model to learn how to fill in these values.

Crosstab Representation

Latent Factors with Crosstab

```python
class DotProductBias(Module):
  def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):
    self.user_factors = Embedding(n_users, n_factors)
    self.user_bias = Embedding(n_users, 1)
    self.movie_factors = Embedding(n_movies, n_factors)
    self.movie_bias = Embedding(n_movies, 1)
    self.y_range = y_range
  def forward(self, x):
    users = self.user_factors(x[:,0])
    movies = self.movie_factors(x[:,1])
    res = (users * movies).sum(dim=1, keepdim=True)
    res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])
    return sigmoid_range(res, *self.y_range)

model = DotProductBias(n_users, n_movies, 50)
learn = Learner(dls, model, loss_func=MSELossFlat())
learn.fit_one_cycle(5, 5e-3)

```

Learn Before

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