streamsight.algorithms

The algorithms module in streamsight contains a collection of baseline algorithms and various of the item-based KNN collaborative filtering algorithm. A total of 3 variation of the item-based KNN algorithm is implemented in the module. Which are listed below

Algorithm

Base class for all algorithms. Programmer should inherit from this class when implementing a new algorithm. It provides a common interface for all algorithms such that the expected methods and properties are defined to avoid any runtime errors.

Algorithm()

Base class for all streamsight algorithm implementations.

Baseline Algorithms

The baseline algorithms are simple algorithms that can be used as a reference point to compare the performance of the more complex algorithms. The following baseline algorithms are implemented in the module.

Random([K, seed])	Random recommendation for users.
Popularity([K])	Recommends K most popular items

Item Similarity Algorithms

Item similarity algorithms exploit relationships between items to make recommendations. At prediction time, the user is represented by the items they have interacted with. 3 variations of the item-based KNN algorithm are implemented in the module. Each variation is to showcase the difference in the learning and prediction of the algorithm. We note that no one algorithm is better than the other, and it greatly depends on the dataset and parameters used in the algorithm which would yield the best performance.

ItemKNN([K])	Item K Nearest Neighbours model.
ItemKNNIncremental([K])	Incremental version of ItemKNN algorithm.
ItemKNNRolling([K])	Rolling version of ItemKNN algorithm.
ItemKNNStatic([K])	Static version of ItemKNN algorithm.