Retargeting (also known as retarget advertising or remarketing) is a form of online display advertising that can help advertisers to display ads to people who have previously visited their websites. It works as follows. Whenever a user visits the advertiser’s website, the user's behavior logs such as views and orders are collected with anonymized user id. If the user visits other online ad media such as websites and apps, his/her previously viewed items or their related items are shown on ad inventory (this is called impression). If the user clicks the ad, he or she is led to the advertiser’s website and may purchase items, which is called conversion.
In the field of online retargeting, retargeting ads are designed to maximize not conversions but clicks. However, the ultimate goal of advertisers is to maximize conversions with limited marketing budget. Therefore, an ad server needs to deliver retargeting ads to those who are likely to make a conversion. From this point of view, RECOBELL believes that conversion probability prediction is a core technical part of retarget advertising.
The task is to develop an algorithm that predicts the probability of conversion when retargeting ads are shown to users. We provide view/order logs and product metadata collected from an e-commerce website for user behavior analysis (2016/08/01 ~ 2016/10/01). We also provide training data and test data from the same website’s retargeting ad campaign. The train data contains impression logs during 2016/09/01 ~ 2016/10/01 along with two labels that indicate 1) whether ad is clicked or not, and 2) whether it leads to conversion or not. To evaluate your algorithm, we will provide as test data impression logs during 2016/10/02 ~ 2016/10/4, where each impression does not contain click or conversion label. Contest entries will develop their models to predict conversion probability for each test impression log (click probability is not required). We will evaluate their performance using logarithmic loss. The method that yields the smallest logarithmic loss will win.
The dataset for this competition is provided by RECOBELL and FUTURESTREAM NETWORKS.
All files are gzipped, CSV format. Column information are shown below.
|impression_id||int||AD Impression Id|
|impression_datetime||timestamp(yyyy-MM-dd HH:mm:ss)||Timestamp of log (Timezone : KST)|
|platform||char(1)||Platform of user (1 : iPhone, 2 : Android, 3: iPad)|
|inventory_type||char(1)||Type of Inventory (A, B)|
|app_code||varchar(10)||Media Code, where ad was exposed|
|os_version||varchar(10)||Version of Operating System|
|model||varchar(255)||Model of Mobile Phone|
|network||varchar(10)||Type Connectivity (3G, 4G, WIFI ...)|
|is_click||int||Whether user clicked AD(1) or not(0)|
|is_conversion||int||Whether user buy item(s) after click this AD|
|Field||Valid Value||Note||View Log||Order Log|
|server_time||timestamp(yyyy-MM-dd HH:mm:ss.S)||Timestamp of log (Timezone : KST)||O||O|
|device||char(2)||Device of user (MW : Mobile Web, MI: iPhone/iPad App, MA : Android App)||O||O|
|session_id||char(10)||Browser Session ID (using Session-Cookie)||O||O|
|quantity||int||Quantity of item||O|
|price||int||Product Price (currency : KRW)|
|category1||char(7)||Category Depth 1|
|category2||char(7)||Category Depth 2|
|category3||char(7)||Category Depth 3|
|category4||char(7)||Category Depth 4|
Given ith impression in test data (1 <= i <= N), conversion probability prediction algorithm must produce a probability of conversion, 0 < pi < 1. Let yi be a binary variable indicating whether ith impression leads to conversion. Then, submissions are evaluated using the Logarithmic Loss (smaller is better).
An application will compare a solution file with retarget_test.csv file containing the answers to the test set and results will be presented in an online score board.
January 9, 2017: start of the competition, data sets become available,
In case of any questions please send an email to Jinwoo Park at firstname.lastname@example.org
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