UEO 2014 : The Second Workshop on User Engagement Optimization at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014)
Call For Papers
The Second Workshop on User Engagement Optimization at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014)
Date: Sunday, August 24, 2014
Venue: Sheraton New York Times Square Hotel, New York City, USA
Paper Submissions Deadline: June 10th, 2014
Paper Submission Website: https://cmt.research.microsoft.com/UEO2014/Default.aspx
Acceptance Notification: July 8th, 2014
Call for Papers
Optimizing online user engagement plays a central role in the business success of many industry companies that own or operate Web sites, apps, and other online systems, including major Web search engines, Web portals, social networking websites, e-commerce systems, and numerous mobile/Web app owners. The key idea of online engagement optimization is to discover and leverage collected knowledge about the behavioral patterns of online engagement and provide features, functionality and experiences accordingly to attract users's involvement, facilitate their interaction and enhance the long-term satisfaction. Study on this topic raises tremendous challenges to many disciplines, yet most of which we believe have to date been under-explored. For instance,
1) How to measure both short-term and long-term user engagement based on online traffic? What is the guideline for engagement metric design?
2) How to discover meaningful knowledge about the users, their interests and behavioral patterns and provide customized experiences that are tailored towards their specific needs?
3) How to develop models, algorithms and systems to effectively and efficiently process online user engagement data that are noisy, sparse and incomplete in nature and available at extremely large scale?
4) How to tune, test and validate a specific intervention or design decision based on time-drifting online traffic?
5) How to mitigate the effects of exogenous factors, and to make sense or discover insights from user engagement data to facilitate the design of an intervention or justify the outcome of a particular intervention or design decision?
This workshop aims to connect academic researchers and industrial practitioners who are working on or interested in online engagement optimization. The goal is to provide a forum for industrial practitioners to expose real-world challenges to academic communities and for academic researchers to popularize state-of-art research outcomes to industrial practitioners, and foster collaboration between the two. The workshop will be a full-day event consisting of invited talks of academic research advances and industrial technical showcases on related topics with presentations from contributed submissions.
The workshop topics include but are not limited to:
* User modeling and targeting
- Novel models, algorithms and systems for user interest modeling
- Novel models, algorithms and systems for behavioral targeting, personalization and social / contextual recommendation
- Quality metrics for user modeling and personalization systems
* Online experiments
- Online experiments design
- Hypothesis testing and attribution in online experiments
- Online experiments case studies
* Metrics and measurements
- Principles for engagement metrics design
- Short- and long-term user engagement measurements
- Psychological, sociological and other complications in engagement metric design
- Engagement metric design case studies
* Learning for engagement optimization
- Algorithms, frameworks and systems for large-scale machine learning
- Novel models for engagement prediction, influence inference and ROI optimization
- Novel models, algorithms, and systems for online learning and contextual bandit
* Visualization and attribution
- Novel engagement data visualization tools
- Engagement time series modeling and effect attribution
The workshop includes invited talks by leading researchers in the filed from both industry and academia, presentations by contributed submissions as well as organized and open discussion on user engagement measurements, prediction, optimization and attribution.
We welcome submissions on all topics related to user engagement optimization. Authors are encouraged to submit their original work in either of the following two types:
* Long papers: (= 10 pages (2-columns, ACM SIG format), including all references, figures and appendices. (same as the KDD main conference)
* Short papers: (= 4 pages (2-columns, ACM SIG format), including all references, figures and appendices.
Please submit your paper through: https://cmt.research.microsoft.com/UEO2014/Default.aspx. Submissions should follow the ACM SIG proceedings format http://www.acm.org/sigs/publications/proceedings-templates#aL2 (Option 2). The review process will be single-blind and the authors do *not* need to anonymize their submission. All papers must be in Adobe Portable Document Format (PDF). Please ensure that any special fonts used are included in the submitted documents. At least one author of each accepted paper will be required to present their work at the presentation session. We will pursue a top AI journal special issue with the topics of the workshop if we receive an appropriate number of high-quality submissions.
Papers submission Deadline: June 10th, 2014
Notification of Acceptance: July 8th, 2014
Workshop Date: Sunday, August 24, 2014
All deadlines are due by 11:59pm, Hawaii time (i.e., GMT - 10 hours).
Program Committee (TBD)
Mounia Lalmas -- Principle Research Scientist, Yahoo Labs
Xavier Amatriain -- Research/Engineering Director, Netflix
Georges Dupret -- Senior Research Scientist, Yahoo Labs
Hongning Wang -- Assistant Professor, University of Virginia
Jian Wang -- Senior Applied Researcher, LinkedIn
Gungor Polatkan -- Senior Research Scientist, Twitter Inc.
Dan Zhang -- Research Scientist, Facebook
Praveen Bommannavar -- Data Scientist, Twitter Inc.
Bo Long -- Staff Researcher Scientist, LinkedIn
Liangjie Hong, Research Scientist -- Yahoo Labs
Shuang-Hong Yang, Research Scientist -- Twitter Inc.
Amr Ahmed, Research Scientist -- Google Research