Preference learning has been studied for several decades and has drawn increasing attention in recent years due to its importance in web applications, such as ad serving, search, and electronic commerce. In all of these applications, we observe (often discrete) choices that reflect relative preferences among several items, e.g. products, songs, web pages or documents. Moreover, observations are in many cases censored. Hence, the goal is to reconstruct the overall model of preferences by, for example, learning a general ordering function based on the partially observed decisions.
Choice models try to predict the specific choices individuals (or groups of individuals) make when offered a possibly very large number of alternatives. Traditionally, they are concerned with the decision process of individuals and have been studied independently in machine learning, data and web mining, econometrics, and psychology. However, these diverse communities have had few interactions in the past. One goal of this workshop is to foster interdisciplinary exchange, by encouraging abstraction of the underlying problem (and solution) characteristics.

The workshop is motivated by the two following lines of research:

1. Large scale preference learning with sparse data: There has been a great interest and take-up of machine learning techniques for preference learning in learning to rank, information retrieval and recommender systems, as supported by the large proportion of preference learning based literature in the widely regarded conferences such as SIGIR, WSDM, WWW, and CIKM. Different paradigms of machine learning have been further developed and applied to these challenging problems, particularly when there is a large number of users and items but only a small set of user preferences are provided.

2. Personalization in social networks: recent wide acceptance of social networks has brought great opportunities for services in different domains, thanks to Facebook, LinkedIn, Douban, Twitter, etc. It is important for these service providers to offer personalized service (e.g., personalization of Twitter recommendations). Social information can improve the inference for user preferences. However, it is still challenging to infer user preferences based on social relationship.

As such, we especially encourage submissions on theory, methods, and applications focusing on large-scale preference learning and choice models in social media. In order to avoid a dispersed research workshop, we solicit submissions (papers, demos and project descriptions) and participation that specifically tackle the research areas as below:

  • Preference elicitation
  • Ranking aggregation
  • Choice models and inference
  • Statistical relational learning for preferences
  • Link prediction for preferences
  • Learning Structured Preferences
  • Multi-task preference learning
  • (Social) collaborative filtering

Program Committee:

  • Nir Ailon, Israel Institute of Technology
  • Edwin Bonilla, NICTA-ANU
  • Tiberio Caetano, NICTA - ANU
  • François Caron, INRIA
  • Jonathan Chung-Kuan Huang, Carnegie Mellon University
  • Chris Dance, Xerox Research Centre Europe
  • Jo­hannes Fürnkranz, TU Darmstadt
  • John Guiver, Microsoft Research Cambridge
  • Eyke Hüllermeier, Universität Marburg
  • Hang Li, Microsoft Research Asia
  • Robert Nowak, University of Wisconsin-Madison
  • Filip Radlinski, Microsoft
  • Chu Wei, Yahoo! Labs
  • Markus Weimer, Yahoo! Labs
  • Kai Yu, NEC Labs
  • Zhao Xu, Fraunhofer IAIS

Program Co-Chair:

  • Jean-Marc Andreoli, Xerox Research Centre Europe
  • Cedric Archambeau, Xerox Research Centre Europe
  • Guillaume Bouchard, Xerox Research Centre Europe
  • Shengbo Guo, Xerox Research Centre Europe
  • Kristian Kersting, Fraunhofer IAIS -- University of Bonn
  • Scott Sanner, NICTA-ANU
  • Martin Szummer, Microsoft Research Cambridge
  • Paolo Viappiani, Aalborg University
  • Onno Zoeter, Xerox Research Centre Europe