Feed Ranking Model
Explore how to engineer user and activity features, collect balanced training data, and select appropriate machine learning models for personalized feed ranking. Understand evaluation methods and practical considerations to optimize model performance and user engagement in feed ranking systems.
We'll cover the following...
We'll cover the following...
3. Model
Feature engineering
| Features | Feature engineering | Description |
|---|---|---|
| User profile: job title, industry, demographic, etc. | For low cardinality: Use one hot encoding. Higher cardinality: use Embedding. | |
| Connection strength between users | Represented by the similarity between users. We can also use Embedding for users and measure the distance vector. | |
| Age of activity | Considered as a continuous feature or a binning value depending on the sensitivity of the Click target. | |
| Activity features | Type of activity, |