Choice Models and Preference Learning
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
Workshop homepage: https://sites.google.com/site/cmplnips11/
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