RECOM_UBCF.R

# User-Based Collaborative Filtering

## simple k-nearest
## returns a list with up to k neighbors for each user
# FIXME: check if we have enough neighbors (warning?)
.knn <- function(sim, k)
  lapply(
    1:nrow(sim),
    FUN = function(i)
      head(order(
        sim[i,], decreasing = TRUE, na.last = NA
      ), k)
  )

.BIN_UBCF_param <- list(
  method = "jaccard",
  nn = 25,
  weighted = TRUE,
  sample = FALSE,
  min_matching_items = 0,
  min_predictive_items = 0
)

BIN_UBCF <- function(data, parameter = NULL) {
  p <- getParameters(.BIN_UBCF_param, parameter)

  if (p$sample)
    data <- sample(data, p$sample)

  model <- c(
    list(description = "UBCF-Binary Data: contains full or sample of data set",
      data = data),
    p
  )

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

    ## newdata are userid
    if (is.numeric(newdata)) {
      if (model$sample)
        stop("User id in newdata does not work when sampling is used!")
      newdata_id <- newdata
      newdata <- model$data[newdata,]
    } else if (ncol(newdata) != ncol(model$data))
      stop("number of items in newdata does not match model.")

    ## prediction
    ## FIXME: add Weiss dissimilarity

    sim <- similarity(
      newdata,
      model$data,
      method = model$method,
      min_matching = model$min_matching_items,
      min_predictive = model$min_predictive_items
    )
    ## FIXME: remove self matches!
    if (!is.null(newdata_id))
      sim[cbind(seq(length(newdata_id)), newdata_id)]  <- NA

    neighbors <- .knn(sim, model$nn)

    if (model$weighted) {
      ## similarity of the neighbors
      s_uk <- sapply(
        1:nrow(sim),
        FUN = function(i)
          sim[i, neighbors[[i]]]
      )
      if (!is.matrix(s_uk))
        s_uk <- as.matrix(t(s_uk))

      ## calculate the weighted sum
      ratings <- t(sapply(
        1:nrow(newdata),
        FUN = function(i) {
          ## neighbors ratings of active user i
          r_neighbors <- as(model$data[neighbors[[i]]], "dgCMatrix")
          ## normalize by the sum by the number of neighbors
          drop(as(crossprod(r_neighbors, s_uk[, i]), "matrix")) /
            colSums(!dropNAis.na(r_neighbors))
        }
      ))

    } else{
      ratings <- t(sapply(
        1:nrow(newdata),
        FUN = function(i) {
          colCounts(model$data[neighbors[[i]]])
        }
      ))
    }

    rownames(ratings) <- rownames(newdata)

    ratings <- new("realRatingMatrix", data = dropNA(ratings))
    ## prediction done

    returnRatings(ratings, newdata, type, n)
  }

  ## construct recommender object
  new(
    "Recommender",
    method = "UBCF",
    dataType = class(data),
    ntrain = nrow(data),
    model = model,
    predict = predict
  )
}


.REAL_UBCF_param <- list(
  method = "cosine",
  nn = 25,
  sample = FALSE,
  weighted = TRUE,
  normalize = "center",
  min_matching_items = 0,
  min_predictive_items = 0
)


REAL_UBCF <- function(data, parameter = NULL) {
  p <- getParameters(.REAL_UBCF_param, parameter)

  if (p$sample)
    data <- sample(data, p$sample)

  ## normalize data
  if (!is.null(p$normalize))
    data <- normalize(data, method = p$normalize)

  model <-
    c(list(description = "UBCF-Real data: contains full or sample of data set",
      data = data),
      p)

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

    newdata_id <- NULL

    ## newdata are userid
    if (is.numeric(newdata)) {
      if (model$sample)
        stop("User id in newdata does not work when sampling is used!")
      newdata_id <- newdata
      newdata <- model$data[newdata,]
    } else {
      if (ncol(newdata) != ncol(model$data))
        stop("number of items in newdata does not match model.")

      if (!is.null(model$normalize))
        newdata <- normalize(newdata, method = model$normalize)
    }

    ## predict ratings
    sim <- similarity(
      newdata,
      model$data,
      method = model$method,
      min_matching = model$min_matching_items,
      min_predictive = model$min_predictive_items
    )
    ## FIXME: remove self matches!
    if (!is.null(newdata_id))
      sim[cbind(seq(length(newdata_id)), newdata_id)]  <- NA

    neighbors <- .knn(sim, model$nn)
    # Note: we may get less than k neighbors!

    ## r_ui = r_u_bar + [sum_k s_uk * r_ai - r_a_bar] / sum_k s_uk
    ## k is the neighborhood
    ## r_ai - r_a_bar_ is normalize(r_ai) = newdata

    if (model$weighted) {
      # average ratings weighted by similarity. This is a list so we can have a
      # different number of nn for each user.
      s_uk <- lapply(
        seq(nrow(sim)),
        FUN = function(i)
          sim[i, neighbors[[i]]]
      )

      ratings <- t(sapply(
        seq(nrow(newdata)),
        FUN = function(i) {
          ## Seems like we do not need to check for no nearest neighbor
          #if (length(neighbors[[i]]) < 1)
          #  return(rep(NA_real_, times = ncol(model$data)))

          ## neighbors ratings of active user i
          r_neighbors <- as(model$data[neighbors[[i]]], "dgCMatrix")
          ## normalize by the sum of weights only if a rating is available
          drop(as(crossprod(r_neighbors, s_uk[[i]]), "matrix")) /
            drop(as(crossprod(
              !dropNAis.na(r_neighbors), s_uk[[i]]
            ), "matrix"))

        }
      ))
      ratings[!is.finite(ratings)] <- NA  ### make NaN into NA

    } else{
      ### unweighted average
      ratings <- t(sapply(
        seq(nrow(newdata)),
        FUN = function(i) {
          ## return nothing if we no nearest neighbor
          if (length(neighbors[[i]]) < 1)
            return(rep(NA_real_, times = ncol(model$data)))

          ## neighbors ratings of active user i
          r_neighbors <- as(model$data[neighbors[[i]]], "dgCMatrix")
          ## normalize by the sum of weights only if a rating is available
          colSums(r_neighbors) / colSums(!dropNAis.na(r_neighbors))
        }
      ))
      ratings[!is.finite(ratings)] <- NA_real_  ### make NaN into NA
    }

    ### Note: If no user in the neighborhood has a rating for the item then it is NA!

    rownames(ratings) <- rownames(newdata)
    ratings <- new("realRatingMatrix",
      data = dropNA(ratings),
      normalize = getNormalize(newdata))
    ratings <- denormalize(ratings)

    returnRatings(ratings, newdata, type, n)
  }

  ## construct recommender object
  new(
    "Recommender",
    method = "UBCF",
    dataType = class(data),
    ntrain = nrow(data),
    model = model,
    predict = predict
  )
}


## register recommender
recommenderRegistry$set_entry(
  method = "UBCF",
  dataType = "binaryRatingMatrix",
  fun = BIN_UBCF,
  description = "Recommender based on user-based collaborative filtering.",
  parameters = .BIN_UBCF_param
)


recommenderRegistry$set_entry(
  method = "UBCF",
  dataType = "realRatingMatrix",
  fun = REAL_UBCF,
  description = "Recommender based on user-based collaborative filtering.",
  parameters = .REAL_UBCF_param
)