From online news to online shopping to scholarly research, we are inundated with a torrent of information on a daily basis. With our limited time, money and attention, we often struggle to extract actionable knowledge from this deluge of data. A common approach for addressing this challenge is personalization, where results are automatically filtered to match the tastes and preferences of individual users.

This workshop aims to bring together researchers from industry and academia in order to describe recent advances and discuss future research directions pertaining to the personalization of digital systems, broadly construed. We aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs for personalization.

The driving factor for new opportunities in personalization is the rapid growth and sophistication of online digital systems that users can interact with (and the resulting interaction data). Personalization first gained significant traction as a way to improve the quality of information retrieval and recommender systems. As the diversity of online content has grown, the development of more effective personalized retrieval and recommender systems remains an important goal. In addition, the emergence of new types of digital systems has expanded the opportunities for personalization to be applied to a wider range of interaction paradigms. Examples of new paradigms include data organization services such as CiteULike and Pinterest, online tutoring systems, and question & answer services such as Quora.

Because the primary asset that enables personalization is the wealth of interaction data, machine learning will play a central role in virtually all future research directions. As a premier machine learning conference, NIPS is an ideal venue for hosting this workshop. Interaction data can pose many interesting machine learning challenges, such as the sheer scale, the multi-task nature of personalizing to populations of users, the exploration/exploitation trade-off when personalizing “on-the-fly”, structured prediction such as formulating a lesson plan in tutoring systems, how to interpret implicit feedback for unbiased learning from interaction data, and how to infer complex sensemaking goals from observing fine-grained interaction sequences.


In summary, our technical topics of interest include (but are not limited to):
- Learning fine-grained representations of user preferences
- Large-scale personalization
- Interpreting observable human behavior
- Interactive algorithms for “on-the-fly” personalization
- Learning to personalize using rich user interactions
- Modeling complex sensemaking goals
- Applications beyond conventional recommender systems