This document discusses an activity ranking system for LinkedIn's feed. It describes LinkedIn's large, heterogeneous set of over 40 activity types that must be ranked. It considers straw man approaches like reverse chronological ordering or ranking by social popularity, but notes these do not ensure relevance. The system models each activity with features of the actor, viewer, and their relationship. A machine learning model is trained on a large scale to predict click-through rates and rank activities. The system considers features like freshness, personalization, and past impressions. Bucket testing showed the approach improved over baselines by considering factors like freshness, personalization, and different platforms.

1. Presented By : Ranjith Kumar Bodla
1 Authors: Deepak Agarwal
Bee-Chung Chen
Liang Zhang et al
Activity Ranking in LinkedIn Feed

2.  LinkedIn Feed
 Straw man approaches
 Activities on LinkedIn Feed
 Freshness
 System Architecture
 Model and Features
 Desktop Bucket Test Results
2
Outline

3.  Professional network
 Heterogeneous updates
 More than 40 types.
 Shared articles, job changes
connection updates etc.
 Challenges
 Large scale (313M+ members)
 Relevance & Personalization
 Freshness, diversity, user fatigue
 How do we rank activities?
3
LinkedIn Feed

4.  Reverse chronological
ranking(recency)
 Fresh but not relevant
 Ranking by social
popularity
 Likes, a useful signal
 CTR not monotonically
related
 Not all activities have likes
4
Straw man approaches

5.  Taxonomy
 Each activity represented as a triple (actor type, verb type, object
type)
 Connection : (member, connect, member)
 Opinion: (member, like, article)
 What happens if we simply rank by CTR?
5
Activities on LinkedIn Feed

6.  Connection activities
 Symmetric connection (Member connection)
 Asymmetric connection (Following)
 Informational activities (Messages, Articles, Pictures)
 Profile activities (Profile pictures, job position, contact info)
 Opinion activities (like and comment)
 Site-specific activities
 Unique about LinkedIn - job anniversaries, endorsements of users’ skills
and job recommendations.
6
Activities on LinkedIn Feed

7. 
7
Taxonomy of
Activities on LinkedIn Feed

8.  Two aspects of freshness -
 First, the age of an activity since its creation.
 Second, the number of times a user has seen a
particular item in the past.
 Differ significantly for different users.
 For example, a heavy user might see an activity
several times within the first hour of its lifetime.
8
Freshness

9.  LinkedIn are connected via a professional network, we can study
how the connection relationship between a viewer and an actor
affects whether the viewer would click on an activity of the actor.
 Demographic similarities based on age, gender and education, etc.
 Experience similarities based on the common companies, job
positions, etc.
 Skill similarities based on the common skills and similar skills.
 Geo similarities based on the locations of the viewer and the actor
at different resolutions (e.g., city, state and country).
 Social network similarities based on connections of the viewer and
the actor.
9
Connection Relationship

10. 10

11.  Training data collection
 Requires randomization
 Personalization features
 E.g., viewer type affinity, viewer actor affinity
 Large scale logistic regression via Alternating
Direction Method of Mulitpliers (ADMM)
 Scalable, distributed algorithm
 Offline evaluation
 Unbiased estimation via replay
11
Relevance via CTR prediction

12. 12
System Architecture

13.  A score is required to rank the activities in the candidate
pool.
 To predict the CTR of each activity, learn a logistic
regression model.
 y denote the click on the activity described by feature vector
x then the corresponding Bernoulli random variable Y is
modeled as
 θ is the parameter vector which we want to learn.
13
Model and Features

14. 
14
Large Scale Logistic Regression
Via ADMM

15. 
15
Large Scale Logistic Regression
Via ADMM

16. 
16
Large Scale Logistic Regression
Via ADMM

17.  This approach typically require greedily picking the
next activity based on the current set of items.
 It is also important to keep the feed fresh.
 we perform an additional exponential decay on the
score based on the age of activities.
 Repeated impressions of the same activity is also
discouraged by introducing an additional decay factor
that takes into account past impression counts of the
activity.
17
Reranking

18.  Effect of freshness.
 Impression Discounting.
 Model Personalization.
 Combining All The Features.
18
Desktop Bucket Test Results

19.  Good ranking model are found by continuously testing of
different models with different features.
 The models learned from data may not have all the
characteristics we desire
 Different platforms (such as mobile vs desktop) have
different characteristics.
 Personalizing our models for user’s tastes and behavior often
gives large improvements in performance.
19
Conclusion

20. 20 Questions ?