Motivation and Goals
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.
Submission
We welcome the following types of papers:

Research papers that introduce new models or methodology, or apply established models/methods to novel domains and data sets; or,
Research papers that explore theoretical and computational issues.

We encourage submissions from a wide range of disciplines, from machine learning to HCI to the social sciences. Topics of interest include (but are not limited to):

Learning of 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

Submissions should be 4-8 pages long, and adhere to the NIPS format. Please make the author information visible.

Submissions will be accepted online here.

Deadline for submissions: October 9, 2014 [11:59pm Honolulu time]
Notification of decisions: October 23, 2014

Organizers
Khalid El-Arini (Facebook)
Yisong Yue (Caltech)
Dilan Görür (Microsoft)