We invite you to take part in one of the four competitions described below. Winners and runners-up will be invited as guests to attend the WSDM Cup Workshop at WSDM 2019 in Melbourne Australia on February 15 2019. A range of cash prizes will also be on offer, with details in each task page.
Task 1: ByteDance - Fake News Classification
ByteDance is a China-based global Internet technology company. Their goal is to build a global content platform that enables people to enjoy various content in different forms, with an emphasis on informing, entertaining, and inspiring people across language, culture, and geography. One of the challenges ByteDance faces is to combat different types of fake news, here referring to all forms of false, inaccurate, or misleading information. As a result, ByteDance has created a large database of fake news articles, and any new article must go through a test for content truthfulness before being published, based on matching between the new article and the articles in the database. Articles identified as containing fake news are then withdrawn after human verification of their status. The accuracy and efficiency of the process, therefore, are crucial in regard to making the platform safe, reliable, and healthy. ByteDance invites researchers and students in the community to take part in the following task. Given the title of a fake news article A and the title of a coming news article B, participants are asked to classify whether B may contain fake news.
Task 2: Spotify - Sequential Skip Prediction Challenge
Spotify is an online music streaming service with over 180 million active users interacting with a library of over 35 million tracks. A central challenge for Spotify is to recommend the right music to each user. While there is a large body of work on recommender systems, there is very little work, or data, describing how users sequentially interact with the streamed content they are presented with. In particular within music, the question of if, and when, a user skips a track is an important implicit feedback signal. Spotify have released a dataset and challenge to WSDM in the hope of spurring research on this important and understudied problem in streaming. The challenge focuses on the task of session-based sequential skip prediction, that is, predicting given a user’s preceding interactions, if they will skip the next tracks encountered in a session.
Task 3: Baidu - Retention Rate of Baidu Hao Kan APP Users
Baidu Hao Kan App is an aggregation platform that provides users with massive high-quality short video content. It provides full coverage of high-quality videos such as fun, music, film, entertainment, games, life, and essays. Baidu Hao Kan App uses intelligent algorithms to understand users' interests and preferences and to recommend tailored video content to users. In the process of rapid growth, Baidu Hao Kan App faces new challenges. New users may download the app to browse and play the video for a while. Some new users will use the app again to watch the video the next day (referred to as “retained” users); however, others no longer use the app. The challenge is to identify factors that increase the percentage of user retention and determine reasons that affect user retention.
Register now at http://dianshi.baidu.com/competition/24/rule
Task 4: Sichuan Airlines - Intelligent Flight Schedules
Sichuan Airlines Co., Ltd was established on August 29, 2002 and is headquartered in Chengdu, China. It has branches in Chongqing, Yunnan and operational bases in Harbin, Beijing, Hangzhou, Xi'an, Sanya, Tianjin, Urumqi, and Xichang. It now operates over 160 flight routes. Sichuan Airlines is facing a number of challenges in scheduling its large number of flights, such as severe weather and aircraft servicing, which may cause large-scale flight delays. When large-scale delays occur, the dispatcher must adjust flight schedules in a timely and effective manner. The purpose of this task is to design and implement an algorithm to automatically identify flights that may be delayed, and to recommend an optimization scheme. For example, when extreme weather causes large-scale delays at multi-bases, the algorithm should automatically identify the subsequent flights that may be delayed and to recommend an optimal flight replacement plan under various practical constraints.
Register now at http://dianshi.baidu.com/competition/25/rule
|Competitions begin||Mid November, 2018|
|Competitions end and result notification||See each task page|
|Workshop paper submission deadline||January 11, 2019|
|WSDM Cup workshop at WSDM 2019 in Melbourne, Australia||February 15, 2019|
WSDM Cup Chairs
|Zhifeng Bao, RMIT Universityemail@example.com|
|Jianzhong Qi, The University of Melbournefirstname.lastname@example.org|