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What Difference Does Personalization Make?
Dilan Gorur · Romer Rosales · Olivier Chapelle · Dorota Glowacka

Mon Dec 09 07:30 AM -- 06:30 PM (PST) @ Harvey's Emerald Bay 4
Event URL: https://sites.google.com/site/nips13personalization/ »

Location: Harvey's Emerald Bay 4

Morning Session
07:30 - 07:40 Welcome and introduction

07:40 - 08:20 Kilian Weinberger - Feature Hashing for Large Scale Classifier Personalization

08:20 - 08:45 Laurent Charlin - Leveraging user libraries to bootstrap collaborative filtering

08:45 - 09:00 Poster spotlight presentations

09:00 - 09:30 Coffee break and poster session

09:30 - 10:10 Susan Dumais - Personalized Search: Potential and Pitfalls

10:10 - 10:30 Discussion, followed by poster session

10:30 – 15:30 Lunch + Skiing

Afternoon Session
15:30 - 16:10 Deepak Agarwal - Personalization and Computational Advertising at LinkedIn

16:10 - 16:35 Jason Weston - Nonlinear Latent Factorization by Embedding Multiple User Interests

16:35 - 17:05 Impromptu talks (new discussion topics and ideas encouraged)

17:05 - 17:45 Coffee break + Posters

17:45 - 18:25 Nando de Freitas - Recommendation and personalization: A startup perspective

18:25 - 19:00 Panel Discussion and wrap up


Personalization has become an important research topic in machine learning fueled in part by its major significance in e-comerce and other businesses that try to tailor to user-specific preferences. Online products, news, search, media, and advertisement are some of the areas that have depended on some form of personalization to improve user 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, to learning 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. We aim to motivate these and new discussions to foster innovation in the area of machine learning for personalization. Research efforts on this topic outside of the NIPS community could provide useful insights into developing new methods and points of view. 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?
* Real-time model adaptation and evaluation approaches. Online learning of personalization models. 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?

Author Information

Dilan Gorur (DeepMind)
Romer Rosales (LinkedIn)
Olivier Chapelle (Google)
Dorota Glowacka (University of Helsinki)

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