Machine learning model to recommend Facebook friend's
Jupyter Notebook
Facebook-friend-recommedation
Machine learning model to recommend Facebook friend's
Problem statement:
Given a directed social graph, we have to predict missing links to recommend friends/connnections/followers (Link Prediction in graph).
Data Overview
Dataset from facebook's recruting challenge on kaggle: https://www.kaggle.com/c/FacebookRecruiting
Data contains two columns: source and destination edge pairs in the directed graph.
Data columns (total 2 columns):
source_node int64
destination_node int64
Business objectives and constraints:
No low-latency requirements.
Predciting the probability of a link is useful so as to recommend the highest probability links to a user.
We got to suggest connnections which are most likley to be correct and we should try and not miss out any connnections.
Performance metric for supervised learning:
Both precision and recall are important, hence F1 score is good choice