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Mastering Feed Ranking Models with Machine Learning
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Learn about the Feed Ranking system architecture and the model requirements
We will cover the following topic
Model Engg
- Feature engineering
- Training data
- Selection
- Evaluation
ML Feed Ranking Model Overview
The ML Feed Ranking Model personalizes user feeds (e.g., LinkedIn feed) to maximize engagement (e.g., Click-Through Rate, CTR). The model ranks content based on user preferences and interactions, incorporating features, data, and algorithms
1.Feature engineering
Feature engineering involves creating features to represent user behavior, activity, and relationships effectively.
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Feature extraction in Python:
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from…