Motivation and Goals<br>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.<br>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.<br>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):<br>Learning of fine–grained representations of user preferences<br>Large–scale personalization<br>Interpreting observable human behavior<br>Interactive algorithms for "on–the–fly" personalization<br>Learning to personalize using rich user interactions<br>Modeling complex sensemaking goals<br>Applications beyond conventional recommender systems<br>
Abbrevation
PMA
City
Montreal
Country
Canada
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