Repeat Buyers Prediction CompetitionIJCAI COMPETITION 2015 CALL FOR PARTICIPANTSRepeat Buyers Prediction after Sales PromotionIntroduction IJCAI is pleased to announce a large-scale machine learning competition, hosted by Alibaba Group, a gold sponsor. This competition aims to promote applications of advanced techniques from AI research to real-world problems. Contestants will have access to vast amount of data provided by Tmall.com, the largest B2C platform in China. Top three winners will be invited to present their results at an IJCAI workshop and get a chance to test their algorithms online. In April 2015, participants all over the world will be invited to play with real transaction data from Tmall.com. The goal is to apply advanced and sophisticated machine learning and data mining techniques to predict which shoppers would become repeat buyers after sales promotion. The main differences from most other AI competitions in the past are listed below: 1. A large sales promotion data set for public usage It is the time to demonstrate your brilliant ideas in the real world! Merchants sometimes run big promotions (e.g., discounts or cash coupons) on particular dates (e.g., Boxing-day Sales, "Black Friday" or "Double 11 (Nov 11th)" , in order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are one-time deal hunters, and these promotions may have little long lasting impact on sales. To alleviate this problem, it is important for merchants to identify who can be converted into repeated buyers. By targeting on these potential loyal customers, merchants can greatly reduce the promotion cost and enhance the return on investment (ROI). It is well known that in the field of online advertising, customer targeting is extremely challenging, especially for fresh buyers. However, with the long-term user behavior log accumulated by Tmall.com, we may be able to solve this problem. In this challenge, we provide a set of merchants and their corresponding new buyers acquired during the promotion on the "Double 11" day. Your task is to predict which new buyers for given merchants will become loyal customers in the future. In other words, you need to predict the probability that these new buyers would purchase items from the same merchants again within 6 months. The competition consists of two stages: Data Description The data set contains anonymized users' shopping logs in the past 6 months before and on the "Double 11" day, and the label information indicating whether they are repeated buyers. Due to privacy issue, data is sampled in a biased way, so the statistical result on this data set would deviate from the actual of Tmall.com. Nevertheless, it will not affect the applicability of the algorithm. In the first stage, the data set is available for downloading, while it is not in the second one. Details of the data can be found in the table below.
Evaluation Metric The Area Under the ROC Curve (AUC), true positive versus false positive is employed as evaluation metric. It can be calculated as (1-e), where 'e' denotes the portion of incorrect pairs (i.e. a negative sample is ranked ahead a positive one). More information can be found at "wikipedia".
April 1, 2015: Competition announcement First Stage First Prize: 4,000USD Second Stage Only the top 50 teams at the first stage are qualified for the second stage. Extra Online Competition The top 3 teams at the second stage will have the opportuntiy to deploy their algorithms on Tmall.com for the ''Double-11'' promotion, 2015. And the winner will be awarded by 50,000USD.
For more information, please contact Alibaba Contest Organizer: Wenliang Zhong at This email address is being protected from spambots. You need JavaScript enabled to view it." target="_blank">yice.zwl@alibaba-inc.com |