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Baseline

Baseline for recommender systems.

A first-order approximation of the bias involved in target. The model equation is defined as:

\[\hat{y}(x) = \bar{y} + bu_{u} + bi_{i}\]

Where \(bu_{u}\) and \(bi_{i}\) are respectively the user and item biases.

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

  • optimizer

    Type → optim.base.Optimizer | None

    Default → None

    The sequential optimizer used for updating the weights.

  • loss

    Type → optim.losses.Loss | None

    Default → None

    The loss function to optimize for.

  • l2

    Default → 0.0

    regularization amount used to push weights towards 0.

  • initializer

    Type → optim.initializers.Initializer | None

    Default → None

    Weights initialization scheme.

  • clip_gradient

    Default → 1000000000000.0

    Clips the absolute value of each gradient value.

  • seed

    Default → None

    Random number generation seed. Set this 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_optimizer (optim.base.Optimizer)

    The sequential optimizer used for updating the user bias weights.

  • i_optimizer (optim.base.Optimizer)

    The sequential optimizer used for updating the item bias 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.Baseline(optimizer=optim.SGD(0.005))

for x, y in dataset:
    _ = model.learn_one(**x, y=y)

model.predict_one(user='Bob', item='Harry Potter')

6.538120

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

  1. Matrix factorization techniques for recommender systems ↩