I’m one of the contributors to this project. The idea of the tool is to focus on typical ML feature engineering challenges. It takes a stream of business events like clicks and impressions, and computes a ton of common ML features on top:
* Parse User-Agent field, make a GeoIP lookup
* Count number of clicks over different items on multiple time windows, like 1-2-3-4 weeks
* Conversion and CTR rates
* Basic customer profiling, like “you clicked on a red item in the past, and this item is also red”
There is just a LambdaMART with xgboost inside, no rocket science. It won’t replace an in-house highly-focused solution, but building everything from scratch may take a ton of time. With Metarank you can quickly hack a good enough solution in a day, hopefully :)
* Parse User-Agent field, make a GeoIP lookup
* Count number of clicks over different items on multiple time windows, like 1-2-3-4 weeks
* Conversion and CTR rates
* Basic customer profiling, like “you clicked on a red item in the past, and this item is also red”
There is just a LambdaMART with xgboost inside, no rocket science. It won’t replace an in-house highly-focused solution, but building everything from scratch may take a ton of time. With Metarank you can quickly hack a good enough solution in a day, hopefully :)