BiasedMF¶
Biased Matrix Factorization for recommender systems.
The model equation is defined as:
Where \(bu_{u}\) and \(bi_{i}\) are respectively the user and item biases. The last term being simply the dot product between the latent vectors of the given user-item pair:
where \(k\) is the number of latent factors.
This model expects a dict input with a user
and an item
entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.
Parameters¶
-
n_factors – defaults to
10
Dimensionality of the factorization or number of latent factors.
-
bias_optimizer (optim.Optimizer) – defaults to
None
The sequential optimizer used for updating the bias weights.
-
latent_optimizer (optim.Optimizer) – defaults to
None
The sequential optimizer used for updating the latent weights.
-
loss (optim.losses.Loss) – defaults to
None
The loss function to optimize for.
-
l2_bias – defaults to
0.0
Amount of L2 regularization used to push bias weights towards 0.
-
l2_latent – defaults to
0.0
Amount of L2 regularization used to push latent weights towards 0.
-
weight_initializer (optim.initializers.Initializer) – defaults to
None
Weights initialization scheme.
-
latent_initializer (optim.initializers.Initializer) – defaults to
None
Latent factors initialization scheme.
-
clip_gradient – defaults to
1000000000000.0
Clips the absolute value of each gradient value.
-
seed (int) – defaults to
None
Randomization seed used for reproducibility.
Attributes¶
-
global_mean (stats.Mean)
The target arithmetic mean.
-
u_biases (collections.defaultdict)
The user bias weights.
-
i_biases (collections.defaultdict)
The item bias weights.
-
u_latents (collections.defaultdict)
The user latent vectors randomly initialized.
-
i_latents (collections.defaultdict)
The item latent vectors randomly initialized.
-
u_bias_optimizer (optim.Optimizer)
The sequential optimizer used for updating the user bias weights.
-
i_bias_optimizer (optim.Optimizer)
The sequential optimizer used for updating the item bias weights.
-
u_latent_optimizer (optim.Optimizer)
The sequential optimizer used for updating the user latent weights.
-
i_latent_optimizer (optim.Optimizer)
The sequential optimizer used for updating the item latent weights.
Examples¶
>>> from river import optim
>>> from river import reco
>>> dataset = (
... ({'user': 'Alice', 'item': 'Superman'}, 8),
... ({'user': 'Alice', 'item': 'Terminator'}, 9),
... ({'user': 'Alice', 'item': 'Star Wars'}, 8),
... ({'user': 'Alice', 'item': 'Notting Hill'}, 2),
... ({'user': 'Alice', 'item': 'Harry Potter'}, 5),
... ({'user': 'Bob', 'item': 'Superman'}, 8),
... ({'user': 'Bob', 'item': 'Terminator'}, 9),
... ({'user': 'Bob', 'item': 'Star Wars'}, 8),
... ({'user': 'Bob', 'item': 'Notting Hill'}, 2)
... )
>>> model = reco.BiasedMF(
... n_factors=10,
... bias_optimizer=optim.SGD(0.025),
... latent_optimizer=optim.SGD(0.025),
... latent_initializer=optim.initializers.Normal(mu=0., sigma=0.1, seed=71)
... )
>>> for x, y in dataset:
... _ = model.learn_one(x, y)
>>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter'})
6.489025
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_one
Fits to a set of features x
and a real-valued target y
.
Parameters
- x (dict)
- y (numbers.Number)
Returns
Regressor: self
predict_one
Predicts the target value of a set of features x
.
Parameters
- x (dict)
Returns
Number: The prediction.