lenskit.algorithms.knn.user

User-based k-NN collaborative filtering.

Functions

score_items_with_neighbors(items, nbr_rows, ...)

Classes

UserRatings(index, ratings, mean)	Dense user ratings.
UserUser(nnbrs[, min_nbrs, min_sim, feedback])	User-user nearest-neighbor collaborative filtering with ratings.

class lenskit.algorithms.knn.user.UserUser(nnbrs, min_nbrs=1, min_sim=1e-06, feedback='explicit', **kwargs)

Bases: Predictor

User-user nearest-neighbor collaborative filtering with ratings. This user-user implementation is not terribly configurable; it hard-codes design decisions found to work well in the previous Java-based LensKit code.

Parameters:

• nnbrs (int) – the maximum number of neighbors for scoring each item (None for unlimited).

• min_nbrs (int) – The minimum number of neighbors for scoring each item.

• min_sim (float) – Minimum similarity threshold for considering a neighbor. Must be positive; if less than the smallest 32-bit normal (\(1.175 \times 10^{-38}\)), is clamped to that value.

• feedback (Literal['explicit', 'implicit']) –

  Control how feedback should be interpreted. Specifies defaults for the other settings, which can be overridden individually; can be one of the following values:

  explicit

  Configure for explicit-feedback mode: use rating values, center ratings, and use the weighted-average aggregate method for prediction. This is the default setting.

  implicit

  Configure for implicit-feedback mode: ignore rating values, do not center ratings, and use the sum aggregate method for prediction.

• center – whether to normalize (mean-center) rating vectors. Turn this off when working with unary data and other data types that don’t respond well to centering.

• aggregate – the type of aggregation to do. Can be weighted-average or sum.

• use_ratings – whether or not to use rating values; default is True. If False, it ignores rating values and treates every present rating as 1.

IGNORED_PARAMS = ['feedback']

Names of parameters to ignore in get_params().

EXTRA_PARAMS = ['center', 'aggregate', 'use_ratings']

Names of extra parameters to include in get_params(). Useful when the constructor takes **kwargs.

users_: Vocabulary

The index of user IDs.

items_: Vocabulary

The index of item IDs.

user_means_: Tensor | None

Mean rating for each known user.

user_vectors_: Tensor

Normalized rating matrix (CSR) to find neighbors at prediction time.

user_ratings_: Tensor

Centered but un-normalized rating matrix (COO) to find neighbor ratings.

fit(data, **kwargs)

“Train” a user-user CF model. This memorizes the rating data in a format that is usable for future computations.

Parameters:

• ratings (pandas.DataFrame) – (user, item, rating) data for collaborative filtering.

• data (Dataset)

Return type:

Self

predict_for_user(user, items, ratings=None)

Compute predictions for a user and items.

Parameters:

• user – the user ID

• items (array-like) – the items to predict

• ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, will be used to recompute the user’s bias at prediction time.

Returns:

scores for the items, indexed by item id.

Return type:

pandas.Series

class lenskit.algorithms.knn.user.UserRatings(index, ratings, mean)

Bases: NamedTuple

Dense user ratings.

Parameters:

• index (int | None)

• ratings (Tensor)

• mean (float)

index: int | None

Alias for field number 0

ratings: Tensor

Alias for field number 1

mean: float

Alias for field number 2