Recommending What Video to Watch Next: A Multitask Ranking System

1. Recommending What Video to Watch Next:
A Multitask Ranking System
Google Inc, 2019
Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Jumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi
November 10, 2021
Presenter: Hongkyu Lim

2. Contents
• Introduction
• Related Work
• Problem Description
• Model Architecture
• Experiment
• Experiment Result
• Miscellaneous

3. 2
Introduction
• Youtube’s recommendation system in two steps
 Candidate Generation
 Ranking  What we are focusing on.
• Ranking
 What to optimize?  be cautious on ‘ setting Objectives’
• Not simply consider ‘Watches’, and ‘Clicks’
• Consider ‘Durations’, ‘Shares’, and ‘Preferences’ to optimize ranks
 Do not be fallen into a trap of ‘Selection Bias’  so called ‘Feedback loop’
• A efficient method needed to deal with ‘Selection Bias’(Also called ‘Position Bias’)
Solution is a use of “Multitask Neural Network”.

4. 3
Introduction
• Architecture
 Consults on Wide&Deep model
 Applys Multi-gate Mixture-of-Exports
(MMoE)
• Why?
 Operating Multitask Learning by segmenting
objectives
• What to click? biased to fishing
• Query and candidate video features
• content, topic, title, upload time
-------------------------------
• How long you watch?
• How much you like?
• User and context features
• Time, user profile
<WIDE> <DEEP>
<Main model>

5. 4
Introduction
• Architecture
 Consults on Wide&Deep model
 Applys Multi-gate Mixture-of-Exports
(MMoE)
• Why?
 Operating Multitask Learning by segmenting
objectives
• What to click? biased to fishing
• Query and candidate video features
• content, topic, title, upload time
-------------------------------
• How long you watch?
• How much you like?
• User and context features
• Time, user profile
<Main model>

6. 5
Introduction
• Objectives
1. Engagement objective
• Users' ‘Clicks’
• How much users are engaged?
2. Satisfaction objective
• How much users like the video?
• Users’ Ratings
<Main model>

7. 6
Introduction
• Model
1. User utility with No Bias
• Query and candidate video features
• User and context features
2. Estimated Propensity Score
• Input : Selection Bias
• Wide section in Wide&Deep
<User Utility>
<Main model>
<Propensity>

8. 7
Related Work
• Industrial Recommendation Systems
 Implicit Feedback > Explicit Feedback
 Dividing states
• Candidate Generation
• Association rule
• co-occurrence
• collaborative filtering(preference)
• Ranking
• Learning-to-rank
• point-wise  efficient in speed
• pair-wise & list-wise
• Modeling Biases in Training Data
 Feedback Loop
• Solution : Passing values as missing values at serving

9. 8
Problem Description
• Multimodal Feature Space
 User utility at candidate level
• Video content, Thumbnail, Sounds, Title, User demo
• Scalability
 Massive users and data

10. 9
Model Architecture
• Wide & Deep
• Multi-gate Mixture-of-Experts
(MMoE)
 Engagement
• Classification : 'Clicks’
• Regression : ‘Watching Durations’
 Satisfaction
• classification : ‘Likes’
• Regression : ‘Ratings’
<WIDE> <DEEP>
<Main model>

11. 10
Model Architecture
• Wide & Deep
• Multi-gate Mixture-of-Experts
(MMoE)
 Engagement
• Classification : 'Clicks’
• Regression : ‘Watching Durations’
 Satisfaction
• Classification : ‘Likes’
• Regression : ‘Ratings’
<Main model>

12. 11
Model Architecture
• Wide & Deep
• Multi-gate Mixture-of-Experts
(MMoE)
 Engagement
• Classification : 'Clicks’
• Regression : ‘Watching Durations’
 Satisfaction
• classification : ‘Likes’
• Regression : ‘Ratings’
<Main model>

13. 12
Model Architecture
• Wide & Deep
• Multi-gate Mixture-of-Experts
(MMoE)
 Engagement
• Classification : 'Clicks’
• Regression : ‘Watching Durations’
 Satisfaction
• classification : ‘Likes’
• Regression : ‘Ratings’
<Main model>

14. 13
Model Architecture
• Why MMoE used?
 Correlation issue
• When correlation between tasks is low, hard-parameter sharing techniques harm the learning
of multiple objectives.

15. 14
Model Architecture
• Selection Bias
 Linear Combination
• Position Feature
+
• Other Features(e.g. device info)
<Main model>

16. 15
Model Architecture
• Selection Bias
 Linear Combination
• Position Feature
+
• Other Features(e.g. device info)
 Penalties on higher ranked videos
 10% dropout for not relying on
Wide part too much

17. 16
Experiment
• Model
 TensorFlow
 TPU
• Deployed and Monitored on Youtube
 Up-to-date
 Offline experiments – monitoring AUC(area under the curve, 수신자 조작 특성)
 A/B Test
 Used to tune hyper-parameters

18. 17
Experiment Result
• Model performance with MMoE and without MMoE
• Visualization of expert utilization(Gating network distribution)
• CTR comparison related to Wide feature(position bias)

19. 18
Experiment Result
• Model performance with MMoE and without MMoE
• Visualization of expert utilization(Gating network distribution)
• CTR comparison related to Wide feature(position bias)

20. 19
Miscellaneous
• Why this paper?

21. Thank you
Any questions?