Ordinal Preference datasets are used by many research communities including, but not limited to, those who work with recommender systems, computational social choice, voting systems and combinatorial optimization.
The prefio R package provides a set of functions which enable users to perform a wide range of preference analysis tasks, including preference aggregation, pairwise comparison summaries and convenient IO operations. This makes it easier for researchers and other professionals to perform common data analysis and preprocessing tasks with such datasets.
The package may be installed from CRAN via
install.packages("prefio")The development version can be installed via
# install.packages("remotes")
remotes::install_github("fleverest/prefio")prefio provides a convenient interface for processing data from tabular formats as well as sourcing data from one of the unified PrefLib formats, including a convenient method for downloading data files directly from PrefLib to your R session.
Preference data can come in many forms. Commonly preference data will be either represented in either long-format with each row corresponding to a particular ranking chosen for a single item:, e.g:
| ID | ItemName | Rank |
|---|---|---|
| 1 | A | 1 |
| 1 | B | 2 |
| 1 | C | 3 |
| 2 | A | 3 |
| 2 | B | 2 |
| 2 | C | 1 |
| 3 | A | 2 |
| 3 | B | 1 |
| 3 | C | 3 |
Three orderings on items {A, B, C} in long-format.
This data can be converted from a data.frame into a preferences
object:
long <- data.frame(
ID = rep(1:3, each = 3),
ItemName = LETTERS[rep(1:3, 3)],
Rank = c(1, 2, 3, 3, 2, 1, 2, 1, 3)
)
prefs <- preferences(long,
format = "long",
id = "ID",
item = "ItemName",
rank = "Rank"
)
print(prefs)## [1] [A > B > C] [C > B > A] [B > A > C]
Another way of tabulating orderings is with each unique ordering on a single row, with each column representing the rank given to a particular item:
| A | B | C |
|---|---|---|
| 1 | 2 | 3 |
| 3 | 2 | 1 |
| 2 | 1 | 3 |
Three orderings on items {A, B, C} in a “rankings” format.
This data can be converted from a data.frame into a preferences
object:
rankings <- matrix(
c(
1, 2, 3,
3, 2, 1,
2, 1, 3
),
nrow = 3,
byrow = TRUE
)
colnames(rankings) <- LETTERS[1:3]
prefs <- preferences(rankings,
format = "ranking"
)
print(prefs)## [1] [A > B > C] [C > B > A] [B > A > C]
The Netflix Prize was a competition devised by Netflix to improve the accuracy of its recommendation system. To facilitate this they released ratings about movies from the users of the system that have been transformed to preference data and are available from PrefLib, (Bennett and Lanning 2007). Each data set comprises rankings of a set of 3 or 4 movies selected at random. Here we consider rankings for just one set of movies to illustrate the functionality of prefio.
PrefLib datafiles such as these can be downloaded on-the-fly by
specifying the argument from_preflib = TRUE in the read_preflib
function:
netflix <- read_preflib("netflix/00004-00000138.soc", from_preflib = TRUE)
head(netflix)## preferences frequencies
## 1 [Beverly Hills Cop > Mean Girls > M ...] 68
## 2 [Mean Girls > Beverly Hills Cop > M ...] 53
## 3 [Beverly Hills Cop > Mean Girls > T ...] 49
## 4 [Mean Girls > Beverly Hills Cop > T ...] 44
## 5 [Beverly Hills Cop > Mission: Impos ...] 39
## 6 [The Mummy Returns > Beverly Hills ...] 37
Each row corresponds to a unique ordering of the four movies in the
dataset. The number of Netflix users that assigned each ordering is
given in the frequencies column. In this case, the most common
ordering (with 68 voters specifying the same preferences) is the
following:
print(netflix$preferences[1], width = 100)## [1] [Beverly Hills Cop > Mean Girls > Mission: Impossible II > The Mummy Returns]
prefio provides a convenient interface for writing preferential
datasets to PrefLib formats. To aid the user, the preferences()
function automatically calculates metrics of the dataset which are
required for producing valid PrefLib files. For example, we can write
our prefs from earlier:
write_preflib(prefs)## Warning in write_preflib(prefs): Missing `title`: the PrefLib format requires a title to be specified. Using `NA`.
## Warning in write_preflib(prefs): Missing `publication_date`, using today's date(2023-06-14).
## Warning in write_preflib(prefs): Missing `modification_date`, using today's date(2023-06-14).
## Warning in write_preflib(prefs): Missing `modification_type`: the PrefLib format requires this to be specified. Using
## `NA`.
## # FILE NAME: NA
## # TITLE: NA
## # DESCRIPTION:
## # DATA TYPE: soc
## # MODIFICATION TYPE: NA
## # RELATES TO:
## # RELATED FILES:
## # PUBLICATION DATE: 2023-06-14
## # MODIFICATION DATE: 2023-06-14
## # NUMBER ALTERNATIVES: 3
## # NUMBER VOTERS: 3
## # NUMBER UNIQUE ORDERS: 3
## # ALTERNATIVE NAME 1: A
## # ALTERNATIVE NAME 2: B
## # ALTERNATIVE NAME 3: C
## 1: 1,2,3
## 1: 3,2,1
## 1: 2,1,3
Note that this produces four warnings. Each warning corresponds to a field which is required by the official PrefLib format, but may not be necessary for internal use-cases. If your goal is to publish some data to PrefLib, these warnings must be resolved.
The New South Wales Legislative Assembly Election Dataset uses prefio to process the public election datasets into PrefLib formats.
The R package elections.dtree uses prefio for tracking ballots observed by the Dirichlet-tree model.
Bennett, J., and S. Lanning. 2007. “The Netflix Prize.” In Proceedings of the KDD Cup Workshop 2007, 3–6. ACM.