# k-NN Collaborative Filtering¶

LKPY provides user- and item-based classical k-NN collaborative Filtering implementations. These lightly-configurable implementations are intended to capture the behavior of the Java-based LensKit implementations to provide a good upgrade path and enable basic experiments out of the box.

## Item-based k-NN¶

class lenskit.algorithms.item_knn.ItemItem(nnbrs, min_nbrs=1, min_sim=1e-06, save_nbrs=None, center=True, aggregate='weighted-average')

Item-item nearest-neighbor collaborative filtering with ratings. This item-item 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 (double) – minimum similarity threshold for considering a neighbor

• save_nbrs (double) – the number of neighbors to save per item in the trained model (None for unlimited)

• center (bool) – 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.

item_index_

the index of item IDs.

Type

pandas.Index

item_means_

the mean rating for each known item.

Type

numpy.ndarray

item_counts_

the number of saved neighbors for each item.

Type

numpy.ndarray

sim_matrix_

the similarity matrix.

Type

matrix.CSR

user_index_

the index of known user IDs for the rating matrix.

Type

pandas.Index

rating_matrix_

the user-item rating matrix for looking up users’ ratings.

Type

matrix.CSR

fit(ratings, **kwargs)

Train a model.

The model-training process depends on save_nbrs and min_sim, but not on other algorithm parameters.

Parameters

ratings (pandas.DataFrame) – (user,item,rating) data for computing item similarities.

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, they may be used to override or augment the model’s notion of a user’s preferences.

Returns

scores for the items, indexed by item id.

Return type

pandas.Series

## User-based k-NN¶

class lenskit.algorithms.user_knn.UserUser(nnbrs, min_nbrs=1, min_sim=0, center=True, aggregate='weighted-average')

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 (double) – minimum similarity threshold for considering a neighbor

• center (bool) – 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.

user_index_

User index.

Type

pandas.Index

item_index_

Item index.

Type

pandas.Index

user_means_

User mean ratings.

Type

numpy.ndarray

rating_matrix_

Normalized user-item rating matrix.

Type

matrix.CSR

transpose_matrix_

Transposed un-normalized rating matrix.

Type

matrix.CSR

fit(ratings, **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.

Returns

a memorized model for efficient user-based CF computation.

Return type

UUModel

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