Building personalized ranking systems is hard.
It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools.
Metarank makes it easy not only for Amazon to do personalization, but for everyone else.Try it out!
Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.
No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration.
Run Metarank API service, feed it with real-time events and receive personalized ranking for your items that will boost conversion, click-through rate or any other business-critical metric you define.
Metarank is not your basic run-of-the-mill vendor locked Python Machine Learning framework: it just needs a YAML definition of your events and data to work.
Metarank is built on top of the shoulders of giants and integrates popular Machine Learning and data processing libraries into a human-friendly personalization tool:
Metarank is not vendor-locking your data: in "explain mode" it can show you all the data used to compute the final ranking.
Metarank not only does ranking, but also helps with full data preprocessing like initial bootstrapping, model training and has more advanced feature extractors.
Metarank supports Flink-based clustered mode for both data import and inference, but it can also be run in a local self-contained mode from command-line, using local disk for storage and bundled single-node cluster.
Metarank is an Alpha: it's early days of development. It is well-covered with tests and runs in production several systems serving real traffic, although we don't recommend yet to run it without developer support.
Metarank cannot replace a proper Data Science team, and probably will not outperform a custom-made solution. But like in a Paretto principle, it can provide you a 80% value with 20% effort. With Metarank you can be up and running with state-of-the-art personalization in a matter of hours.
As we're only shaping the form of the final software right now, you can contribute by trying to run Metarank on your dataset and report bugs. You can also contact us to describe your personalization use case, so we can tune our development priorities according to your feedback.