PrefLib

PrefLib is a reference library of preference data maintained by Nicholas Mattei and Simon Rey. The original version of this website was developed by Nicholas Mattei and Toby Walsh.

We aim at providing a comprehensive resource for the multiple research communities that deal with preferences: computational social choice, recommender systems, data mining, machine learning, combinatorial optimization, to name just a few.

The strength of PrefLib is to provide carefully curated data, formatted in a unified format. We encourage the users to read the detailed explanations that we provide regarding the format and the modelisation choices. Once everything is clear, feel free to explore the datasets we are hosting, or to search for specific files that may interest you.

PrefLib-Tools

Providing data is only the first step, the next one being actually using the data. To help you with that, we provide a Python library specifically designed to work with PrefLib instances: the PrefLib-Tools. This library is distributed in PyPi.

Have a look at the preflibtools repository, where you can download the code; and check out the documentation!

Data Usage and Citation Policy

Constructing and maintaining this website and its database requires a lot of work. We ask that you provide a reference to our website when publishing research based on data gathered here. Here are some references you can use.

  • Nicholas Mattei and Toby Walsh. PrefLib: A Library of Preference Data. Proceedings of Third International Conference on Algorithmic Decision Theory (ADT 2013) — PDFBibtex.
  • Nicholas Mattei and Toby Walsh. A Preflib.org Retrospective: Lessons Learned and New Directions. Trends in Computational Social Choice — PDFBibtex.

In addition, many dataset have specific citation requirements. Make sure to always include them whenever you used a file taken from such a dataset (especially if you downloaded aggregated data files).

Contributing to PrefLib

We rely on the support of the community in order to increase the usefulness and coverage of this site. If you want to donate a new dataset, report an issue with an existing dataset, or suggest changes to the website, several GitHub repositories are at your disposal.

If you need anything, have a look at those repositories, open new issues, comments, like, subscribe and share the word!

In Brief

We currently host:

  • 8 types of data
  • 56 datasets
  • 13540 data files
  • More than 1.5 GB of data

Other Links

Here are some links that you might find relevant as well.

  • DEMOCRATIX: A Declarative Approach to Winner Determination
  • Pnyx: An Easy to Use Aggregation Tool
  • Whale4: Which Alternative is Elected?
  • VoteLib: A Library of Voting Behavior
  • Pabulib: A Library of Participatory Budgeting Instances
  • CRISNER: A Qualitative Preference Reasoner
  • Spliddit: Quick and Easy Solutions to Fair Division Problems
  • RoboVote: AI Driven Decisions

To find more data check these websites.

The community

We want to thank all who participated in the development of PrefLib. This list may not be exhaustive, but we hope it is. Contact us if you feel unjustly treated.

These Papers are Using PrefLib

Below is a list of papers that have made use of or directly referenced data stored here at PrefLib. The papers have been automatically added from Google Scholar, if there is a problem with a paper or if you want to add one paper, please contact us.

Even more references, papers, and tutorials can be found in the proceedings of the EXPLORE Workshops:

Some Tools to Work with Preferences

Empirical experiments with real data are becoming a more fundamental part of work in computational social choice. In addition to a lightweight set of tools for working with data from PrefLib we also host documentation for several of these project. Please contact Nick if you have code that you would like to share with the community.

Iterative Voting Simulator

This is a voting simulator built for the paper A Local-Dominance Theory of Voting Equilibria. We are releasing its source code to be expanded and enhanced by the community. However, it is quite versatile in its current construction, and can be used for various simulations "as is".

Learn more.

Kidney Dataset Generator

Kidney failure is a life-threatening health issue that affects hundreds of thousands of people worldwide. In the US alone, the waitlist for a kidney transplant has over 100,000 patients. This list is growing: demand far outstrips supply.This codebase includes: structural elements of kidney exchange like "pools", "hospitals", and "pairs", a couple of kidney exchange graph generators, a couple of kidney exchange solvers (max weight, failure-aware, fairness-aware, individually rational), and a dynamic kidney exchange simulator.

Learn more.

CRISNER: A Qualitative Preference Reasoner for CP-nets, TCP-nets, CP-Theories

CRISNER stands for Conditional and Relative Importance Statement Network PrEference Reasoner. It can reason about ceteris paribus preference languages such as CP-nets, TCP-nets and CP-theories. Given a preference specification (a set of preference statements) in one of these languages, CRISNER succinctly encodes its induced preference graph (IPG) into a Kripke structure model in the language of the NuSMV model checker. This Kripke model is reachability-equivalent to the induced preference graph. CRISNER generates the model only once, and then translates each query posed against this preference specification into a temporal logic formula in computation-tree temporal logic (CTL) such that the formula is verified in the Kripke model if and only if the query holds true according to the ceteris paribus semantics of the preference language. The model checker either affirms the query or returns with a counterexample. For answering queries related to equivalence and subsumption checking of two sets of preferences, CRISNER constructs a combined IPG and uses temporal queries in CTL to identify whether every dominance that holds in one also holds in the other, and vice-versa.

Learn more.