Choice Models and Preference Learning

Preference learning has been studied for several decades and has drawn increasing attention in recent years due to its importance in diverse applications such as web search, ad serving, information retrieval, recommender systems, electronic commerce, and many others. In all of these applications, we observe (often discrete) choices that reflect preferences among several entities, such as documents, webpages, products, songs etc. Since the observation then is partial, or censored, the goal is to learn the complete preference model, e.g. to reconstruct a general ordering function from observed preferences in pairs.

Traditionally, preference learning has been studied independently in several research areas, such as machine learning, data and web mining, artificial intelligence, recommendation systems, and psychology among others, with a high diversity of application domains such as social networks, information retrieval, web search, medicine, biology, etc. However, contributions developed in one application domain can, and should, impact other domains. One goal of this workshop is to foster this type of interdisciplinary exchange, by encouraging abstraction of the underlying problem (and solution) characteristics during presentation and discussion. In particular, 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, 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, Linkin, 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 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
Discrete choice models and inference
Statistical relational learning for preferences
Link prediction for preferences
Learning Structured Preferences
Multi-task preference learning


Important Dates:

Paper submission deadline: 3 November 2011 (Extended)
Author notification: 5 November 2011
Final paper due: 1 December 2011
Workshop date: 17 December 2011


Submission Instructions:

We solicit extended abstracts using the NIPS style files, preferably 2 to 4 pages, but no more than 8 pages. Submissions should include the title, authors' names, and email addresses. We will post the final version of the papers on the workshop web page and encourage authors to post their contribution on arXiv.

Papers should be submitted to the EasyChair system at https://www.easychair.org/conferences/?conf=cmpl2011.

We are seeking funds to publish the talks on http://videolectures.net/.