[Apology for cross-postings]

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Repeat Buyers Prediction Competition

IJCAI COMPETITION 2015 CALL FOR PARTICIPANTS

Repeat Buyers Prediction after Sales Promotion

Introduction

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

2. A free distributed computation platform for top teams

3. A great opportunity to deploy algorithms online for winners

It is the time to demonstrate your brilliant ideas in the real world!

Problem Definition

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:

In the first stage, a data set containing around 200k users is given
for training, while the other of similar size for testing. Similar to
other competitions, you may extract any features, then perform
training with additional tools. You need to only submit the prediction
results for evaluation.

In the second stage, the top 50 teams from the first stage will have
the opportunity to work on a much larger data set on Alibaba's cloud
platform. You will need to submit your code in JAVA, then the
distributed computation will be handled by the cloud platform.

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.

Data Fields

Definition

user_id

A unique id for the shopper.

age_range

User's age range: 0 for <18; 1 for [18,24]; 2 for [25,34]; 3 for
[35,54]; 4 for >=55.

gender

User's gender: 0 for female, 1 for male.

merchant_id

A unique id for the merchant.

label

Value from {0, 1, -1, NULL}. '1' denotes 'user_id' is a repeat buyer
for 'merchant_id', while '0' is the opposite. '-1' represents that
'user_id' is not a new customer of the given merchant, thus out of our
prediction. However, such records may provide additional information.
'NULL' occurs only in the testing data, indicating it is a pair to
predict.

activity_log

A set of interaction records between {user_id, merchant_id}, where
each record is an action represented as
'item_id:category_id:brand_id:time_stamp:action_type'. '#' is used to
separate two neighbouring elements. Records are not sorted in any
particular order.

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".


Important Dates:

 April 1, 2015: Competition announcement

 April 15, 2015: Competition begins

 May 15, 2015: First Stage Competition ends

 May 18, 2015: Second Stage Competition begins

 June 20, 2015:Second Stage Competition ends

 June 30, 2015: Final result announcement


Awards

- First Stage

First Prize: 4,000USD

Second Prize: 3,000USD

Third Prize: 2,000USD

- Second Stage

Only the top 50 teams at the first stage are qualified for the second stage.

First Prize: 6,000USD

Second Prize: 4,000USD

Third Prize: 2,000USD

The top 3 teams may present their solutions at the IJCAI workshop
"Social Inference Analysis", with additional 3,000USD as registration
and travelling allowance.

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.

Remark1: The problem would be related but different from the first two
competitions. Detail will be announced before September 2015.

Remark2: Participants at this stage would work onsite as interns for
around two months. Besides the award, salary and housing allowances
will be also provided.



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