Item-based k-nearest neighbors in rrecsys

Given a target user and her positively rated items, the algorithm will identify the k-most similar items for each target a and will rank them according to aggregated similarities with the different targets as described by Sarwar et al. For similarity computation among two co-rated items the “adjusted cosine” is applied. This implementation is based on the implementation in recommenderlab by Michael Hahsler.

To train a model with this algorithm is required to define an additional argument, neigh the neighborhood size.

ibknn <- rrecsys(smallML, "ibknn", neigh = 20)
## Neighborhood calculated in:  0.6234109 seconds.
ibknn
## The model was trained on the dataset using  IBKNN algorithm. 
## The algorithm was configured with the following neighborhood width: 20

The neigh default value is 10.

The returned object is of type IBclass.

To get more details about the slots read the reference manual.