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Facebook-Friend-Recommendation

This is a friend recommendation system used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media platforms and a directed edges (or 'links') indicates that one person 'follows' the other, or are 'friends' on social media. Now, the task is to predict newer edges to be offered as 'friend suggestions'.

Problem Statement

Given a directed social graph, have to predict missing links to recommend users. (Link Prediction in Graph)

Data Overview

Dataset Link

Taken data from facebook's recruting challenge on Kaggle Data contains two columns source and destination eac edge in graph.

  • Data columns (total 2 columns):
    • source_node int64
    • destination_node int64

Mapping the problem into supervised learning problem:

Real-world Objectives and Constraints

  • No low-latency requirement.
  • Probability of prediction is useful to recommend ighest probability links

Performance metric for supervised learning:

  • Both precision and recall is important so F1 score is good choice
  • Confusion matrix

Solution Approach

Decision Tree based approached proved to be quite effective for this problem statement and since the number of features constructed is not too large, bagging and boosting approaches could be easily employed for high precision and easy training.

Here are the details and performance metrics of the classifiers used :

Model No. of Base Learners Max Depth of Base Learners Training F1-score Testing F1-score
Random Forest 121 14 0.964 0.921
XGBoost 109 10 0.992 0.926