DLP-KDD 2019: 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2019 Dena’ina Convention Center and William Egan Convention Center, Anchorage, Alaska USA Anchorage, AK, United States, August 5, 2019

In the increasingly digitalized world, it is of utmost importance for various applications to harness the ability to process, understand, and exploit data collected from the Internet.For instance, in customer-centric applicationssuch as personalized recommendation, online advertising, and search engines,interest/intention modeling from customers’ behavioral datacan not only significantly enhance user experiences but also greatly contribute to revenues. Recently, we have witnessed that Deep Learning-based approaches began 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 many applications with dense data such as image classification and speech recognition where Deep Learning-based approaches have been extensively studied. For example, the training samples of a typical click-through rate (CTR) prediction task often involve billions of sparse features, how to mine, model and inference from such data becomes an interesting problem, and how to leverage such data in Deep Learning could be a new research direction. The characteristics of such data pose uniquechallengesto 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 the challenges, opportunities, and 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=dlpkdd2019.

List of Topics

Topics include but are not limited to deep learning based network architecture design, large scale deep learning training framework, high-performance online inference engine or toolkits that help breaking the black box of deep learning models, such as 

• 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)

• Jian Xu (Tech Lead of advertising group, Alibaba)

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

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

• Jun Lang (Tech Lead of data science group, Lazada)

• Wenwu Ou (Tech Lead of recommendation system group, Alibaba) 

• Hongbo Deng (Tech Lead of search engine group, Alibaba)

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

• John F. Canny (Professor of UC Berkeley)

Venue

The workshop will be held in conjunction with the KDD2019 conference, in Anchorage, AK, August 4-8, 2019.

Contact

All questions about submissions should be emailed to the organizing committee at dlpkdd2019@gmail.com.

Sponsors

• Alibaba