Description
The Earlybird Light Ranker README provides a good overview of the current system, but one possible improvement would be to include a section on the limitations and challenges faced by the current model. This can help people understand the reasons behind the ongoing work to replace the stack and provide context for future improvements.
Here's some things that could also be added to that section to make it more clear:
While the Earlybird Light Ranker has been effective in ranking tweets, it has some limitations and challenges that will be addressed in the new model. Some of these issues include:
Feature Engineering: The current model relies on manual feature engineering, which may not capture complex relationships between variables and can be time-consuming to maintain. The new model should ideally utilize more advanced feature learning techniques, such as deep learning, to automatically learn and extract relevant features.
Scalability: The Earlybird Light Ranker's logistic regression model may not scale well with the increasing volume of tweets and user interactions on the platform. Future models should focus on improved scalability to handle the growing dataset.
Real-time Processing: The current model has some limitations in handling real-time features, such as social engagements, which may affect the accuracy of the ranking. Future models should prioritize real-time processing to ensure that tweet rankings are as up-to-date as possible.
Other comments can be added to the readme (https://github.com/twitter/the-algorithm/blob/main/src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/README.md) including maybe about how the model handles bias and fairness and it's drawbacks. I feel like this is a crucial part of understanding this model, and would be incredibly helpful to include in the readme.