Image Feature Learning for Cold Start Problem in Display Advertising

Kaixiang Mo, Bo Liu, Lei Xiao, Yong Li, Jie JiangHKUST

Tencent Inc

Display Advertisements

• Display Ads are important income sources• Ads are sold using Cost-Per-Click, So improving

Click-Through-Rate is core tasks.

What computer sees

• User info, Ads info, Historical Click logs• Cannot recommend New Ads

Box 2Games

20 clicks

Box 1Clothes25 clicks

from teens

Box 30 clicks

Male, 20-30 years, etc

Cold Start for New Ads

• New Ads are important• Users are easily tired of old

ads• Increasing number of sellers• Ads have short life

expectance

• Extracting image feature could alleviate cold start

Can we distinguish high CTR ads? • Problem: Find ad image that are most likely to be

clicked based on image content.

High CTR ads Low CTR ads

Related Image Features

• SIFT features [Lowe, 1999]• For Object recognition• Rotation Invariant

• Multi-media features [Cheng et al., 2012]• Brightness, Sharpness, Color, interest point, etc• Fixed, Requires much human effort designing

Handcrafted features are not enough• Handcrafted features (lighting, color, sharpness,

etc)• Task dependent

• Cannot capture key factor for CTR• Inflexible

• Key factors might change in future• Heuristic

• Hard to design, prone to error

• Automatically Feature Learning is Necessary!

Deep Convolutional Neural Networks• Learn image feature directly from raw pixel and

click log• No human heuristic• Could learn discriminative and meaningful feature

Deep Convolutional Neural Networks• Confined Model for less background noise/few object• Position of element matters• Speed up using simplified aggregated instances• <Ad#, click>, …, <Ad#, noclick> = <Ad#, N click, M noclick>• Handles 47 billion instances on single machine

Experiment

• Rank Ad image according to predicted CTR in completely new ads.• Evaluation: AUC• Baseline:

• Multimedia-feature• SIFT + BOW/LLC

• Setting• Image features only• Image features combined with Basic features.

Qzone Ads #Instance #Ads

Training 45 billion 220,000Testing 2.4 billion 33,000 new ads

Feature Number Feature description

Ad ID 250,000 Unique ID for ads

Ad Category 5 Categories of ads

Ad Position 5 Display position of ads

Better Prediction on Ad-images CTR• Feature Learning Method improves baselines by as

much as 2% on AUC.

CNN can learn discriminative and meaningful feature• Visualizing important areas

Original Ads Important Areas

More Visualizations

• Mostly noisy human face and promotion text

End

• Q&A