R/HybridRecommender.R

In audachang/recommenderlab.test: Lab for Developing and Testing Recommender Algorithms

## create a hybrid recommender


HybridRecommender <- function(..., weights = NULL) {
  recommender <- list(...)

  if(is.null(weights)) weights <- rep(1, length(recommender))
  else if(length(recommender) != length(weights)) stop("Number of recommender and length of weights does not agree!")
  weights <- weights/sum(weights)

  if(!all(sapply(recommender, is, "Recommender"))) stop("Not all supplied models are of class 'Recommender'.")

  model <- list(recommender = recommender, weights = weights)

  predict <- function(model=NULL, newdata, n=10,
    data= NULL, type=c("topNList", "ratings", "ratingMatrix"), ...) {

    type <- match.arg(type)

    ## newdata are userid
    if(is.numeric(newdata)) {
      if(is.null(data) || !is(data, "ratingMatrix"))
        stop("If newdata is a user id then data needes to be the training dataset.")
      newdata <- data[newdata, , drop = FALSE]
    }

    #if(ncol(newdata) != length(model$labels)) stop("number of items in newdata does not match model.")

    pred <- lapply(model$recommender, FUN = function(object)
      object@predict(object@model, newdata, data=data, type="ratings", ...))

    ratings <- matrix(NA, nrow=nrow(newdata), ncol = ncol(newdata))
    for(i in 1:nrow(pred[[1]])) {
      ratings[i,] <- colSums(t(sapply(pred, FUN = function(p)
        as(p[i,], "matrix"))) * model$weights, na.rm = TRUE)
    }

    ratings <- as(ratings, "realRatingMatrix")
    colnames(ratings) <- colnames(newdata)


    if(type == "ratingMatrix")
      stop("Hybrid cannot predict a complete ratingMatrix!")

    returnRatings(ratings, newdata, type, n)
  }

  ## this recommender has no model
  new("Recommender", method = "HYBRID",
    dataType = "ratingMatrix",
    ntrain = NA_integer_,
    model = model,
    predict = predict)
}