topN: Rank top-N highest-predicted items for an existing user

In poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood

Rank top-N highest-predicted items for an existing user

Description

Rank top-N highest-predicted items for an existing user

Usage

topN(
  model,
  user,
  n = 10,
  include = NULL,
  exclude = NULL,
  output_score = FALSE,
  nthreads = parallel::detectCores()
)

Arguments

model	A Poisson factorization model as returned by 'poismf'.
user	User for which to rank the items. If 'X' passed to 'poismf' was a 'data.frame', must match with the entries in its first column, otherwise should match with the rows of 'X' (numeration starting at 1).
n	Number of top-N highest-predicted results to output.
include	List of items which will be ranked. If passing this, will only make a ranking among these items. If 'X' passed to 'poismf' was a 'data.frame', must match with the entries in its second column, otherwise should match with the columns of 'X' (numeration starting at 1). Can only pass one of 'include' or 'exclude.' Must not contain duplicated entries.
exclude	List of items to exclude from the ranking. If passing this, will rank all the items except for these. If 'X' passed to 'poismf' was a 'data.frame', must match with the entries in its second column, otherwise should match with the columns of 'X' (numeration starting at 1). Can only pass one of 'include' or 'exclude'. Must not contain duplicated entries.
output_score	Whether to output the scores in addition to the IDs. If passing 'FALSE', will return a single array with the item IDs, otherwise will return a list with the item IDs and the scores.
nthreads	Number of parallel threads to use.

Details

Even though the fitted model matrices might be sparse, they are always used in dense format here. In many cases it might be more efficient to produce the rankings externally through some library that would exploit the sparseness for much faster computations. The matrices can be access under 'model$A' and 'model$B'.

Value

• If passing 'output_score=FALSE' (the default), will return a vector of size 'n' with the top-N highest predicted items for this user.If the 'X' data passed to 'poismf' was a 'data.frame', will contain the item IDs from its second column, otherwise will be integers matching to the columns of 'X' (starting at 1). If 'X' was passed as 'data.frame', the entries in this vector might be coerced to character regardless of their original type.

• If passing 'output_score=TRUE', will return a list, with the first entry being the vector described above under name 'ix', and the second entry being the associated scores, as a numeric vector of size 'n'.

See Also

topN.new predict.poismf factors.single

poismf documentation built on March 31, 2023, 7:38 p.m.