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')¶ Bases:
lenskit.algorithms.Predictor
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.
-
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)¶ Train a model.
The model-training process depends on
save_nbrs
andmin_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:
-
User-based k-NN¶
-
class
lenskit.algorithms.user_knn.
UserUser
(nnbrs, min_nbrs=1, min_sim=0, center=True, aggregate='weighted-average')¶ Bases:
lenskit.algorithms.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.
-
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)¶ “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:
-