People You May Konw

Problem Statement

People You May Know (PYMK) is a list of users with whom you may want to connect based on things you have in common, such as a mutual friend, school, or workplace.

Clarifying Requirements

1. What’s the objective/motivation?
   • May I assume the motivation of the PYMK feature is to help users discover potential connections and grow their network?
2. How to define ‘connections’?
   • May I assume that people are friends only if each is a friend of the other?
3. What should I consider for forming connections?
   • To recommend potential connections, a huge list of factors must be considered, such as educational background, work experiences, existing connections, historical activities. Should I focus on the most important factors?
4. Define the scale
   • How many connections does an average user have? (like 1000)
   • What’s the total number of users on this platform? (like 1 billion)
   • How many of them are daily active users? (like 300 million)
5. Model Dynamics
   • May I assume that the social graph of most users are not dynamic, meaning that their connections don’t change significantly over a short period.

Frame the Problem as an ML task

• Define the ML Objective
  • Maximize the number of formed connections between users
• Define the input and the output
  • input: a user
  • output: a list of users

Choosing ML methology
There are commonly two approaches for PYMK: Pointwise Learning-To-Rank (LTR) and Edge Prediction.

1. Pointwise LTR

• Why this approach:
  • The pointwise LTR transforms a ranking problem to a supervised learning problem, where the model takes user and a candidate connection pair as inputs, and outputs a score or probability.
  • In cold-start scenarios where rich interaction data is lacking, the pointwise LTR can quickly model the similarity between user pairs.
• Why not this approach:
  • This ignores some social context, the inputs are distinct users

2. Edge Prediction

• The entire social context can be represented as a graph, where each node represents a user, and an edge between two nodes indicates a formed connection between two users.
• Use the entire graph as input, and predict the probability of an edge existing between user A and other users

Data Preparation
We typically use three types of raw data:

• User
• Connections
• Interactions

One challenge with this

Feature Engineering

Model Development

Appendix

Graph-based
In general, a graph represents relations(edges) between a collection of entities(nodes). Graphs can store structural data, there are three general types of prediction tasks that can be performed on structured data represented by graphs:

• Graph-level
  • like chemical compound
• Edge-level
  • like PYMK on social media
• Node-level
  • like predicting whether a person is a spammer or not, given a social network graph