DLP-KDD 2019

Website for 1st International Workshop on Deep Learning Practice for High- Dimensional Sparse Data with KDD 2019

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Image credit: Glenn-Aronwits

Important notice

Time & Location : 1pm - 5pm August 5th, Summit 2 - Ground Level, William Egan Convention Center

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Schedule

Time Event Title
13:00-13:40 Keynote #1 Quan Yu (Ant Financial)
Deep Learning in Ant Credit Pay Fraud Detection
13:40-13:50   Opening intro & Best paper announcement
13:50-14:05 Oral talk #1 Learning over Categorical Data using Counting Features
14:05-14:20 Oral talk #2 Attention-based Mixture Density Recurrent Networks
for History-based Recommendation
14:20-14:30   Poster Spotlight talk * 8 (1min each)
14:30-15:00   Coffee break & Poster session
15:00-15:40 Keynote #2 Peng Cui (Tsinghua University)
Towards Explainable and Stable Prediction
15:40-15:55 Oral talk #3 XDL: An Industrial Deep Learning Framework for High-dimensional Sparse Data
15:55-16:10 Oral talk #4 Automatic Feature Engineering From Very High Dimensional Event Logs Using Deep Neural Networks
16:10-16:25 Oral talk #5 (Best Paper Award) An End-to-End Neighborhood-based Interaction Model
for Knowledge-enhanced Recommendation
16:25-16:40 Oral talk #6 AMAD: Adversarial Multiscale Anomaly Detection
on High Dimensional and Time-Evolving Categorical Data
16:40-16:55 Oral talk #7 Learning Job Representation Using Directed Graph Embedding
16:55-17:00   Ending


All the accepted papers can be found here

Introduction

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 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 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 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 the challenges, opportunities, and new ideas in the practice of Deep Learning on high-dimensional sparse data.

Important Dates

  • Submission deadline: May 14, 2019
  • Acceptance notification: June 1, 2019 By June 3 PDT due to the weekend.
  • Workshop date: August 5, 2019 (Dena’ina Convention Center and William Egan Convention Center, Anchorage, Alaska USA)

Workshop Format and Schedule

Half day. We would like to hold a half-day workshop in order to have enough new ground to cover invited academic and industry talks with a focus on many deep learning related areas. The tentative program schedule includes the following components:

  • Ten oral presentations from paper submissions for 15 minutes each
  • A poster session if there are lots of submissions for 45 minutes
  • Four invited talks with a mix of industry and academia for 1 hour each ! A panel discussion with 45 minutes

Topics of Interest

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 compression for industrial application
  • Scalable, Distributed and Parallel Training System for 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 encountered in real-world applications

Call for papers

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

If you have any questions about submissions or our workshop, please contact dlpkdd2019@gmail.com

Workshop Chairs


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
Shanghai Jiao Tong University

John F. Canny
Professor
UC Berkeley

Program Committee

  • Zhijun Yin, Facebook
  • Rui Li, JD.com
  • Ruiming Tang, Noah’s Ark Lab, Huawei
  • Mingsheng Long, Tsinghua University
  • Zhen Qin, Google
  • Zhe Wang, Hulu
  • Min Zhang, Tsinghua University
  • Xipeng Qiu, Fudan University
  • Qi Zhang, Fudan University
  • Yin Lou, Ant Finance
  • Dheevatsa Mudigere, Facebook
  • Mou Na, Alibaba
  • Xiang Li, Alibaba
  • Bibek Behera, IIT Bombay
  • Han Zhu, Alibaba
  • Jiwei Tan, Peking University
  • Di Wu, Alibaba
  • Junwei Pan, Yahoo
  • Na Ma, Taobao
  • Fei Sun, Alibaba
  • Junqi Jin, Alibaba
  • Xiaofeng Yang, Alibaba
  • Chen Xu, Peking University
  • Tao Zhuang, Alibaba
  • Qingyao Ai, University of Massachusetts Amherst