The mathematical analysis and understanding of rank data has been a fascinating topic for centuries, and has been investigated in disciplines as wide-ranging as social choice/voting theory, decision theory, probability, statistics, and combinatorics. In modern times, huge amounts of data are generated in the form of rankings on a daily basis: restaurant ratings, product ratings/comparisons, employer ratings, hospital rankings, doctor rankings, and an endless variety of rankings from committee deliberations (including, for example, deliberations of conference program committees such as NIPS!). These applications have led to several new trends and challenges: for example, one must frequently deal with very large numbers of candidates/alternatives to be ranked, with partial or missing ranking information, with noisy ranking information, with the need to ensure reliability and/or privacy of the rank data provided, and so on.

Given the increasing universality of settings involving large amounts of rank data and associated challenges as above, powerful computational frameworks and tools for addressing such challenges have emerged over the last few years in a variety of areas, including in particular in machine learning, operations research, and computational social choice. Despite the fact that many important practical problems in each area could benefit from the algorithmic solutions and analysis techniques developed in other areas, there has been limited interaction between these areas. Given both the increasing maturity of the research into ranking in these respective areas and the increasing range of practical ranking problems in need of better solutions, it is the aim of this workshop to bring together recent advances in analyzing rank data in machine learning, operations research, and computational social choice under one umbrella, to enable greater interaction and cross-fertilization of ideas.

A primary goal will be to discover connections between recent approaches developed for analyzing rank data in each of the three areas above. To this end, we will have invited talks by leading experts in the analysis of rank data in each area. In addition, we will include perspectives from practitioners who work with rank data in various applied domains on both the benefits and limitations of currently available solutions to the problems they encounter. In the end, we hope to both develop a shared language for the analysis and understanding of rank data in modern times, and identify important challenges that persist and could benefit from a shared understanding.

The topics of interest include:
- discrete choice modeling and revenue management
- voting and social decision making, preference elicitation
- social choice (rank aggregation) versus individual choice (recommendation systems)
- stochastic versus active sampling of preferences
- statistical/learning-theoretic guarantees
- effects of computational approximations