RandomNormal¶
Predicts random values sampled from a normal distribution.
The parameters of the normal distribution are fitted with running statistics. They parameters are independent of the user, the item, or the context, and are instead fitted globally. This recommender therefore acts as a dummy model that any serious model should easily outperform.
Parameters¶
-
seed
Default →
None
Random number generation seed. Set this for reproducibility.
Attributes¶
-
mean
-
variance
Examples¶
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.RandomNormal(seed=42)
for x, y in dataset:
model.learn_one(**x, y=y)
model.predict_one(user='Bob', item='Harry Potter')
6.147299621751425
Methods¶
learn_one
Fits a user
-item
pair and a real-valued target y
.
Parameters
- user — 'ID'
- item — 'ID'
- y — 'Reward'
- x — 'dict | None' — defaults to
None
predict_one
Predicts the target value of a set of features x
.
Parameters
- user — 'ID'
- item — 'ID'
- x — 'dict | None' — defaults to
None
Returns
Reward: The predicted preference from the user for the item.
rank
Rank models by decreasing order of preference for a given user.
Parameters
- user — 'ID'
- items — 'set[ID]'
- x — 'dict | None' — defaults to
None