1. Image Feature Learning for Cold Start Problem in Display Advertising
Kaixiang Mo, Bo Liu, Lei Xiao, Yong Li, Jie Jiang
HKUST
Tencent Inc

2. Display Advertisements
Display Ads are important income sources
Ads are sold using Cost-Per-Click, So improving Click-Through-Rate is core tasks.

3. What computer sees User info, Ads info, Historical Click logs
Cannot recommend New Ads
Male, years, etc
Box 1
Clothes
25 clicks from teens
Box 2
Games
20 clicks
Box 3
0 clicks

4. 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

5. 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

6. 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

7. 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!

8. Deep Convolutional Neural Networks
Learn image feature directly from raw pixel and click log
No human heuristic
Could learn discriminative and meaningful feature

9. 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

10. 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
220,000
Testing
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
Display position of ads

11. Better Prediction on Ad-images CTR
Feature Learning Method improves baselines by as much as 2% on AUC.

12. CNN can learn discriminative and meaningful feature
Visualizing important areas
Original Ads
Important Areas

13. More Visualizations
Mostly noisy human face and promotion text

14. End
Q&A