lenskit.knn.user

User-based k-NN collaborative filtering.

Functions

score_items_with_neighbors(log, items, ...)

Classes

UserKNNConfig(*[, max_nbrs, min_nbrs, ...])	Configuration for ItemKNNScorer.
UserKNNScorer([config])	User-user nearest-neighbor collaborative filtering with ratings.
UserRatings(index, ratings, mean)	Dense user ratings.

class lenskit.knn.user.UserKNNConfig(*, max_nbrs=20, min_nbrs=1, min_sim=1e-06, feedback='explicit')

Bases: BaseModel

Configuration for ItemKNNScorer.

Parameters:

• max_nbrs (Annotated[int, Gt(gt=0)])

• min_nbrs (Annotated[int, Gt(gt=0)])

• min_sim (Annotated[float, Gt(gt=0)])

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

max_nbrs: PositiveInt

The maximum number of neighbors for scoring each item.

min_nbrs: PositiveInt

The minimum number of neighbors for scoring each item.

min_sim: PositiveFloat

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: FeedbackType

The type of input data to use (explicit or implicit). This affects data pre-processing and aggregation.

property explicit: bool

Query whether this is in explicit-feedback mode.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class lenskit.knn.user.UserKNNScorer(config=None, **kwargs)

Bases: Component[ItemList, …], Trainable

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.

Note

This component must be used with queries containing the user’s history, either directly in the input or by wiring its query input to the output of a user history component (e.g., UserTrainingHistoryLookup).

Stability:

Caller (see Stability Levels).

Parameters:

• config (UserKNNConfig)

• kwargs (Any)

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_: csc_array

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

train(data, options=TrainingOptions(retrain=True, device=None, rng=None))

“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)

• options (TrainingOptions)

class lenskit.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