KDD Cup 2020

KDD Cup is the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining, the leading professional organization of data scientists.


In 2020, due to the growing popularity of different data science competitions types, we decided to continue last year’s tradition with opening 3 competition tracks. All competitions are scheduled to start in March-April 2020. Follow the links and look for the updates from our competition hosts. Total rewards exceeding 120K will be given to the winners and leaders of the competitions this year.

KDD CUP 2020 Tracks

In ML Track 1 “Challenges for Modern E-Commerce Platform, sponsored by Alibaba, Alibaba DAMO Academy, Duke University, Tsinghua University, and UIUC, participants are invited to learn high-quality cross-modal representations by considering complex information of different types and the strong connection between modalities. The learned representation can be then used to compute the similarity score between the representations and select the images/videos that are related to the text. Finally, each submission will be evaluated on the testing dataset, which evaluates the correspondence between the retrieved products and the ground truth. This track has two tasks: Task 1 and Task 2.

Keywords: learning representations, transfer learning, image, video, and text processing

Sponsor: Alibaba DAMO Academy

Platform: Tianchi

Total reward: $40,000

Regular Machine Learning Competition Track (ML Track 2)

In the second Regular Track, “Adversarial Attacks and Defense on Academic Graph”, sponsored by BienData, requires participants to submit a modified version of the original dataset as a form of attack that should look similar to the original graph, but has lower classification accuracy under baseline models. It should be prepared and saved at the backend of the competition system. Then, all teams are required to submit an attacker and a defender. The organizers will match all attackers and defenders from all teams and rank the final leaderboard.

Keywords: generative adversarial networks (GANs), graph-structured data, graph embeddings

Sponsor: Biendata.com and Zhipu.AI- http://www.zhipu.ai/

Total reward: $19,500

Platform: Biendata

Automated Machine Learning Competition Track (AutoML Track)

In the AutoML Track “Automatic Graph Representation Learning (AutoGraph)”, provided by 4Paradigm, ChaLearn, Stanford University and Google, participants are invited to deploy AutoML solutions for graph representation learning, where node classification is chosen as the task to evaluate the quality of learned representations. Each team is given five public datasets to develop AutoML solutions. Five feedback datasets are provided to allow participants to evaluate their solutions. These solutions will be evaluated with five unseen datasets without human intervention, and the winners will be chosen based on the final rankings of the datasets.

Keywords: graph representation learning, graph-structured data, graph embeddings, AutoML

Sponsor: 4Paradigm.com

Total reward: $33,500

Platform: CodaLab

Reinforcement Learning Competition Track (RL Track)

The RL Track “Learning to Dispatch and Reposition on a Mobility-on-Demand Platform”, sponsored by Didi Chuxing in collaboration with DiDi AI Labs, the largest ridesharing platform in the world, requires participants to apply machine learning tools to determine novel solutions for order dispatching (order matching) and vehicle repositioning (fleet management) on a Mobility-on-Demand (MoD) platform. Specifically, the competition looks at how machine learning solutions can be applied to improve the efficiency of MoD platform.

Keywords: reinforcement learning, mobility-on-demand, vehicle repositioning

Sponsor: Didi Chuxing

Total reward: $30,000

Platform: Biendata

A word to the participants

The rules of the competitions are set by the competition hosts – this fact must be acknowledged by contestants entering the competition. Please, address your questions and inquiries to the competition hosts via channels of communication established by them (contacts, moderators, discussion boards, etc.)

You can register your team on each competition platform only once. The team name you pick will be further used in all announcements, press-releases, and the award ceremony, in case your team comes up with a winning solution. Therefore, please choose the name for your team wisely during the registration process.

KDD Conference and KDD Cup are adopting to ongoing pandemic situation which can entail some changes to the ways we held this event over the years. Please, acknowledge this fact and follow the announcements.

KDD Cup Chairs


KDD Cup Chairs

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Jie Tang

Tsinghua University
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Jieping Ye

Didi Chuxing & University of Michigan
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Claudia Perlich

Two Sigma
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Iryna Skrypnyk


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