Background

The topic of preferences has recently attracted considerable attention in Artificial Intelligence (AI) research, notably in fields such as agents, non-monotonic reasoning, constraint satisfaction, planning, and qualitative decision theory. Preferences provide a means for specifying desires in a declarative way, which is a point of critical importance for AI. Drawing on past research on knowledge representation and reasoning, AI offers qualitative and symbolic methods for treating preferences that can reasonably complement hitherto existing approaches from other fields, such as decision theory and economic utility theory. Needless to say, however, the acquisition of preferences is not always an easy task. Therefore, not only are modeling languages and representation formalisms needed, but also methods for the automatic learning, discovery, and adaptation of preferences.

Methods for learning preference models and predicting preferences are among the very recent research trends in fields like machine learning and knowledge discovery. Approaches relevant to this area range from learning special types of preference models, such as lexicographic orders, over collaborative filtering techniques for recommender systems and ranking techniques for information retrieval, to generalizations of classification problems such as label ranking. Like other types of complex learning tasks that have recently entered the stage, preference learning deviates strongly from the standard problems of classification and regression. It is particularly challenging as it involves the prediction of complex structures, such as weak or partial order relations, rather than single values. Moreover, training input will not, as it is usually the case, be offered in the form of complete examples but may comprise more general types of information, such as relative preferences or different kinds of indirect feedback and implicit preference information.

Topics of Interest

This workshop aims at providing a forum for the discussion of recent advances in the use of machine learning and data mining methods for problems related to the learning and discovery of preferences, and to offer an opportunity for researchers and practitioners to identify new promising research directions. Topics of interest include, but are not limited to

  • quantitative and qualitative approaches to modeling preferences as well as different forms of feedback and training data;
  • learning utility functions and related regression problems;
  • preference mining and preference elicitation;
  • learning relational preference models;
  • embedding of other types of learning problems in the preference learning framework (such as label ranking, ordinal classification, or hierarchical classification);
  • comparison of different preference learning paradigms (e.g., "big bang" approaches that use a single model vs. modular approaches that decompose the learning of preference models into subproblems);
  • ranking problems, such as learning to rank objects or to aggregate rankings;
  • scalability and efficiency of preference learning algorithms;
  • methods for special application fields, such as web search, information retrieval, electronic commerce, games, personalization, or recommender systems;
  • connections to other research fields, such as decision theory, operations research, and social choice theory.

Workshop Format

The workshop will be held as a one-day session with paper presentations. As the workshop addresses a quite recent research topic, we also encourage submissions presenting more preliminary results and discussing open problems. Correspondingly, two types of contributions will be solicited, namely short communications (short talks) and full papers (long talks) reporting on mature research results.

Invited Speaker

Prof. Thorsten Joachims (Cornell University) accepted the invitation to given an invited talk.

Publication

Papers must be formatted in Springer LNCS style and submitted in PDF format to one of the organizers. There is no strict page limitation, though 10-15 pages for full papers and 6-8 pages for short communications should be taken as rough guidelines. Authors' instructions along with LaTeX and Word macro files are available at the Springer website.

On-line proceedings will be edited so that the results are widely accessible. If there is sufficient interest and quality of the papers, we will also consider a post-workshop publication (e.g., as a special issue in a journal).

Important Dates

  • Paper deadline: June 23
  • Notifications of Acceptance: July 31
  • Final versions: August 18

Organizers

Program Committee

  • Fabio Aiolli
  • Bernard De Baets
  • Paul Bennett
  • Weiwei Cheng
  • Wei Chu
  • Eibe Frank
  • Toshihiro Kamishima
  • Eneldo Loza Mencia
  • Sang-Hyeun Park
  • Antti Ukkonen
  • Stijn Vanderlooy

Accepted Papers

Program

10:30 - 10:35Welcome and overview
Session A: Preference Learning: Models and Methods
10:35 - 11:05Invited Talk: Eliciting Preferences in Information Retrieval (T. Joachims)
11:05 - 11:30 Supervised Learning as Preference Optimization: Recent Applications (F. Aiolli and A. Sperduti)
11:30 - 11:55 Integrating Expert Knowledge into Kernel-Based Preference Models (W. Waegeman, B. De Baets, and L. Boullart)
11:55 - 12:20 Learning Preference Relations over Combinatorial Domains (J. Lang and J. Mengin)
12:20 - 13:20 Lunch
Session B: Learning to Rank: Methods and Applications
13:20 - 13:45 Learning Preferences with Co-Regularized Least-Squares (E. Tsivtsivadze, F. Gieseke, T. Pahikkala, J. Boberg, and T. Salakoski)
13:45 - 14:10 Choice Based Conjoint Analysis: Discrete Choice Models vs. Direct Regression (B. Taneva, J. Giesen, P. Zolliker, and K. Mueller)
14:10 - 14:35 Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques (A. Bellogin, I. Cantador, P. Castells, and A. Ortigosa)
14:35 - 15:00 Learning SVM Ranking Function from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain (R. Arens)
15:00 - 15:25 Learning to Rank Cases with Classification Rules (J. Zhang, J.W. Bala, A. Hadjarian, and B. Han)
15:25 - 16:00Coffee break
Session C: From Classification to Predicting Structured Outputs
16:00 - 16:25 Statistical Approach to Ordinal Classification with Monotonicity Constraints (W. Kotlowski and R. Slowinski)
16:25 - 16:50Analyzing Ranking Data Using Decision Tree (P.L.H. Yu, W.M. Wan, and P.H. Lee)
16:50 - 17:15 Multi-Label Classification With Contraints (S. Park and J. Fuernkranz)
17:15 - 17:40 Instance-based Label Ranking using the Mallows Model (W. Cheng and E. Huellermeier)

 

Related Workshops

Beyond Binary Relevance: Preferences, Diversity, and Set-Level Judgments at SIGIR-2008, Singapore