embed_pagespace: Build a Starspace model for interest-based recommendation

In ruimtehol: Learn Text 'Embeddings' with 'Starspace'

Build a Starspace model for interest-based recommendation

Description

Build a Starspace model for interest-based recommendation (pagespace). For example a user clicks on a webpage.

Usage

embed_pagespace(
  x,
  model = "pagespace.bin",
  early_stopping = 0.75,
  useBytes = FALSE,
  ...
)

Arguments

x	a list where each list element contains a character vector of pages which the user was interested in
model	name of the model which will be saved, passed on to starspace
early_stopping	the percentage of the data that will be used as training data. If set to a value smaller than 1, 1-early_stopping percentage of the data which will be used as the validation set and early stopping will be executed. Defaults to 0.75.
useBytes	set to TRUE to avoid re-encoding when writing out train and/or test files. See writeLines for details
...	further arguments passed on to starspace except file, trainMode and fileFormat

Value

an object of class textspace as returned by starspace.

Examples

data(dekamer, package = "ruimtehol")
x <- subset(dekamer, !is.na(question_theme))
x <- strsplit(x$question_theme, ",")
x <- lapply(x, FUN=unique)
str(x)
set.seed(123456789)
model <- embed_pagespace(x, dim = 5, epoch = 5, minCount = 10, label = "__THEME__")
plot(model)
predict(model, "__THEME__MARINE __THEME__DEFENSIEBELEID")

pagevectors <- as.matrix(model)

mostsimilar <- embedding_similarity(pagevectors,
                                    pagevectors["__THEME__MIGRATIEBELEID", ])
head(sort(mostsimilar[, 1], decreasing = TRUE), 3)
mostsimilar <- embedding_similarity(pagevectors,
                                    pagevectors["__THEME__DEFENSIEBELEID", ])
head(sort(mostsimilar[, 1], decreasing = TRUE), 3)

ruimtehol documentation built on May 29, 2024, 5:26 a.m.