Dispatcher

Similar to other packages in R (i.e.: proxy, recommenderlab, etc.), rrecsys uses a formal method in combination to a registry to execute a specific function. The non-primitive function rrecsys will process only S4 object of type dataSet and based on its function definition will determine whatever the required algorithms is available in the registry. Resuming, has two main arguments, the dataset (data), and the algorithm name (alg, which isn't case sensitive and might be matched even partially) the rest are the ellipses which depend on the required algorithm:

# Usage
rrecsys(data, alg, ...)

The method rrecsys will train model on the given dataset with the required method and its configuration. In the next vignettes will be given more details about the possible alternative arguments for the dispatcher.

The registry

The registry is a structure that may be used even autonomously. The main function of registry would be to display available recommender algorithms in rrecsys and theirs default configuration. To call it:

rrecsysRegistry
## Regirsty defined for rrecsys with 8 entries as follows:
## 
## Algorithm: BPR
## Reference: S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian Personalized Ranking from Implicit Feedback.
## Parameter and default values:
##    k lambda   regU   regI   regJ updateJ
## 1 10   0.05 0.0025 0.0025 0.0025    TRUE
## 
## Algorithm: IBKNN
## Reference: B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms.
## Parameter and default values:
##   neigh
## 1    10
## 
## Algorithm: itemAverage
## Reference: NA
## No parameter.
## 
## Algorithm: userAverage
## Reference: NA
## No parameter.
## 
## Algorithm: globalAverage
## Reference: NA
## No parameter.
## 
## Algorithm: FunkSVD
## Reference: Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. 
## S. Funk. Netflix Update: Try this at Home.
## Parameter and default values:
##    k gamma lambda
## 1 10  0.01  0.001
## 
## Algorithm: Popular
## Reference: NA
## No parameter.
## 
## Algorithm: wALS
## Reference: R. Pan, Y. Zhou, B. Cao, N.  Liu, R. Lukose, M. Scholz, and Q. Yang.  One-Class Collaborative Filtering.
## Parameter and default values:
##    k lambda scheme delta
## 1 10   0.01  None!  0.04