In the increasingly digitalized world, it is of utmost importance for various applications to harness the ability to process, understand, and exploit massive data collected from the Internet. For instance, in customer-centric applications such as personalized recommendation, online advertising, and search engines, interest/intention modeling from customers’ behavioral data can not only significantly enhance user experiences but also greatly contribute to revenues.

Recently, we have witnessed that deep learning-based approaches has been widely applied to empower these internet-scale applications by better leveraging the massive data. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it different from those applications with dense data processing, such as image classification and speech recognition, where deep learning-based approaches have been extensively studied. One of the main applications is the user-centric platform which consists of great deal of users, items and user generated tabular data which are quite high-dimensional. How to mine, model and inference from such data becomes an interesting problem. Furthermore, leveraging such data with deep learning techniques could be a new research direction with high practical value. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss and formulate the challenges, utilize opportunities, and propose new ideas in the practice of deep learning on high- dimensional sparse data.

Submission Guidelines

Submissions are invited on describing innovative research on real-world data systems and applications, industrial experiences and identification of challenges that deploy research ideas in practical applications. Work-in-progress papers are also encouraged.

Full-length papers (up to 9 pages) or extended abstracts (2-4 pages) are welcome. Submissions must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. 

Reviews are not double-blind, and author names and affiliations should be listed. Please use the KDD official guidelines to format your paper.

All submissions can be made through EasyChair using the following link: https://easychair.org/conferences/?conf=dlpkdd2020.

For all the accepted papers, we provide the option that it could be archived in ACM Digtal Library. You may see the publication of last year at https://dl.acm.org/doi/proceedings/10.1145/3326937

List of Topics

• Large Scale User Response Prediction Modeling
• Representation Learning for High-dimensional Sparse Data
• Embedding techniques, manifold learning and dictionary learning
• User Behaviour Understanding
• Large Scale Recommendation and Retrieval System 
• Model compressionfor industrial application
• Scalable, Distributed and Parallel Training Systemfor Deep Learning
• High throughput and low latency real time Serving System
• Applications of transfer learning, meta learning for sparse data
• Auto Machine Learning, Auto feature selection
• Explainable deep learning for high dimensional data
• Data augmentation, Anomaly Detection for High-dimensional Sparse data
• Generative Adversarial Network for sparse data
• Other challenges encounteredin real-world applications

Committees

Organizing committee

• Xiaoqiang Zhu (Tech Lead of advertising group, Alibaba)

• Kuang-chih Lee (Tech Lead of business intelligence group, AliExpress)

• Guorui Zhou (Algorithm expert of advertising group, Alibaba)

• Biye Jiang (Algorithm expert of advertising group, Alibaba)

• Liang Xiong (Facebook AI Applied Research)

• Junlin Zhang (Tech Lead of Artificial Intelligence Laboratory, Sina Weibo)

• Zhe Wang (Tech Lead of recommendation group, Roku)

• Zheng Wen (Research Scientist, DeepMind)

• Haishan Liu (Director of Engineering, Tencent)

• Kan Ren (Microsoft Research)

• Qingyao Ai (Assistant Professor, University of Utah)

• Shandian Zhe (Assistant Professor, University of Utah)

• Weinan Zhang (Assistant Professor, Shanghai Jiao Tong University)

Venue

The workshop will be held in conjunction with the KDD2020 conference, in San Diego, CA, August 24, 2020.

Contact

All questions about submissions should be emailed to the organizing committee at dlpkddworkshop@gmail.com.

Sponsors