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box::use(
recommenderlab[predict],
methods[as],
dplyr[select]
)
SVD_predict <- function(books_tab, selected_ids, ratings_tab, SVD_model, select_user_mat, how_many) {
found_ids <- selected_ids[selected_ids %in% colnames(ratings_tab)]
if (length(found_ids) == 0) {
return(NULL)
}
ratings_line <- ratings_tab[1,]
ratings_line[] <- NA
ratings_line[found_ids] <- 5
select_user_mat <- as.matrix(ratings_line)
select_user_mat <- as(select_user_mat, 'realRatingMatrix')
predict_SVDF <- predict(SVD_model,
select_user_mat,
type = "topNList",
n = how_many
)
predict_SVDF_list <- as(predict_SVDF, 'list')
predict_SVDF_list <- lapply(predict_SVDF_list, as.numeric)
predict_SVDF_df <- as.data.frame(predict_SVDF_list)
names(predict_SVDF_df) <- "book_id"
recommendations_SVDF <- merge(predict_SVDF_df, books_tab, by = "book_id")
recommendations_tab <- recommendations_SVDF |> select(
title, average_rating, description, url, image_url, genres, author_name
)
recommendations_tab$model <- "SVD"
return(recommendations_tab)
}
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