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.
## 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.