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.
There are two different primary modes that you can use these algorithms in. When using explicit feedback (rating values), you usually want to use the defaults of weighted-average aggregation and mean-centering normalization. This is the default mode, and can be selected explicitly by passing feedback='explicit' to the class constructor.
With implicit feedback (unary data such as clicks and purchases, typically represented with rating values of 1 for positive items), the usual design is sum aggregation and no centering. This can be selected with feedback='implicit', which also configures the algorithm to ignore rating values (when present) and treat every rating as 1:
implicit_knn = ItemItem(20, feedback='implicit')Attempting to center data on the same scale (all 1, for example) will typically produce invalid results. ItemKNN has diagnostics to warn you about this.
The feedback option only sets defaults; the algorithm can be further configured (e.g. to re-enable rating values) with additional parameters to the constructor.
0.14 The feedback option and the ability to ignore rating values was added in LensKit 0.14. In previous versions, you need to specifically configure each option.
lenskit.algorithms.item_knn
This is LensKit's item-based k-NN model, based on the description by :citeDeshpande2004-ht.
ItemItem
lenskit.algorithms.user_knn
UserUser