```python
learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))
learn.fit_one_cycle(5, 5e-3, wd=0.1)
#we can retrieve model summary using this line of code
learn.model
#we can use bias term to see which movies are most popular/high -rating
movie_bias = learn.model.i_bias.weight.squeeze()
idxs = movie_bias.argsort(descending=True)[:5]
[dls.classes['title'][i] for i in idxs]
```

San Diego State University

```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

Related