NIPS 2013 Workshop on Personalization

What Difference Does Personalization Make?

December 9, 2013

Harvey's Emerald Bay 4

Lake Tahoe, Nevada, USA 

Personalization has become an important research topic in machine learning fueled in part by its major significance in e-comerce and other businesses/services that try to tailor to user-specific needs or preferences. Online products, news, search, media, advertisement, user interfaces, and to a lesser extent healthcare, are several of the areas that have depended on some form of personalization to improve satisfaction or business goals in general. In order to address personalization problems machine learning has long relied on tools such as collaborative filtering (matrix factorization) and models originally developed not necessarily for personalization. However, even though the data available for personalization has grown in richness and size, and the available processing power has also increased, the basic tenet for the methods used has not changed in a major way.

It is possible that personalization requires a change in perspective, in order to capture the finer, user-specific details in the data. It may be necessary to develop modeling and evaluation approaches different than those developed for more general purposes, possibly taking into account user behavior and cognitive features. We aim to motivate these and new discussions to foster innovation in the area of machine learning for personalization. This workshop will bring together experts in various fields including machine learning, data mining, information retrieval and social sciences, with the goal of understanding the current state of the art, possible future challenges and research directions. 

An underlying primary theme of this workshop is to debate whether specialized models and evaluation approaches are necessary to properly address the challenges that arise in large scale personalization problems.

The topics of interest include but are not limited to:
  • Is it necessary to develop fundamentally new approaches and evaluation strategies to properly address personalization?
  • What are appropriate objective/evaluation metrics for personalization in various domains (e.g.; ads personalization, news personalization)?
  • How can social network information contribute to personalization?
  • What breaks/what works when moving from small to large-scale personalization?
  • Online learning of personalization models, real-time model adaptation and evaluation approaches. How fast can we learn personalized models?
  • How can learning models address the cold-start problem?
  • Personalization with constraints, such as budget or diversity constraints.
  • Privacy considerations: How much personalization is possible or acceptable?

Confirmed Invited Speakers

Deepak Agarwal - LinkedIn
Susan Dumais - Microsoft Research
Nando de Freitas - University of British Columbia
Kilian Weinberger -  Washington University in St. Louis


Romer Rosales - LinkedIn
Dilan Gorur - Yahoo! Labs
Olivier Chapelle - Criteo

Dorota Glowacka - University of Helsinki